# A Survey on Detection, Classification, and Tracking of Aerial Threats using
Radar and Communications Systems

Wahab Khawaja1, Martins Ezuma1, , Vasilii Semkin3, Fatih Erden1, Ozgur
Ozdemir1, , and Ismail Guvenc1, Email<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>{oozdemi,
<EMAIL_ADDRESS>3VTT Technical Research Centre of Finland, Tietotie 3,
02150 Espoo, Finland  4 Electrical and Computer Engineering Department, North
Carolina State University, Raleigh, NC 27606, USA

###### Abstract

The use of unmanned aerial vehicles (UAVs) for a variety of commercial,
civilian, and defense applications has increased many folds in recent years.
While UAVs are expected to transform future air operations, there are
instances where they can be used for malicious purposes. In this context, the
detection, classification, and tracking (DCT) of UAVs (DCT-U) for safety and
surveillance of national air space is a challenging task when compared to DCT
of manned aerial vehicles. In this survey, we discuss the threats and
challenges from malicious UAVs and we subsequently study three radio frequency
(RF)-based systems for DCT-U. These RF-based systems include radars,
communication systems, and RF analyzers. Radar systems are further divided
into conventional and modern radar systems, while communication systems can be
used for joint communications and sensing (JC&S) in active mode and act as a
source of illumination to passive radars for DCT-U. The limitations of the
three RF-based systems are also provided. The survey briefly discusses non-RF
systems for DCT-U and their limitations. Future directions based on the
lessons learned are provided at the end of the survey.

###### Index Terms:

Classification, communication systems, detection, joint communications and
sensing (JC&S), radar, radio frequency (RF) analyzers, tracking, unmanned
aerial vehicles (UAVs).

##  I Introduction

Radar systems are widely used for the detection, classification, and tracking
(DCT) of aerial vehicles. Radar technology was first introduced in 1935 [1].
The initial radar systems were bulky with large mechanically rotated antennas,
were ground-based, and they had large power requirements, limited processing
capabilities, limited accuracy, high cost. They were also highly vulnerable to
electronic countermeasures (ECM). Radars have seen decades of improvements in
overcoming many challenges. Modern radar systems employ cutting-edge
electronics, such as Gallium Nitride, along with compact antennas, phased
arrays, and highly efficient signal processing techniques. These advancements
are pivotal in achieving remarkable capabilities including extended detection
ranges, rapid response times, streamlined signal processing, exceptional
accuracy, low probability of false alarm (PFA), unambiguous detection and
tracking of aerial vehicles even in cluttered environments, simultaneous
tracking of multiple aerial vehicles, seamless integration with diverse
sensors (airborne, ground, and sea-based), and operational adaptability across
various terrains [2, 3]. Also, the extensive training data of different
terrains and potential aerial vehicles aided with artificial intelligence (AI)
classification algorithms have helped in the real-time classification of
different types of aerial vehicles in complex environments. Furthermore, the
$5$G and beyond are envisioned to use joint sensing and communications [4]
that rely on radar technology.

Figure 1: Detection of a UAV using a conventional pulse radar. Different types
of clutter are also shown. Here $\tau$ denotes time delay and $c$ is the speed
of light.

The unmanned aerial vehicle (UAV) technology is currently one of the fastest-
growing technologies in the world. According to [5], the overall UAV global
market share (in the military, law enforcement, government, commercial, and
consumer domains) is estimated at $$27.4$ billion in $2021$ and is expected to
reach $$58.4$ billion by $2026$. However, there are many incidents around the
world in the recent decade when UAVs are used for malicious purposes, e.g.,
[6, 7]. Malicious UAV detection, tracking, and classification (e.g.,
distinguishing from hobbyist UAVs and birds) are challenging to both
conventional and modern radar systems at desired ranges. This is mainly due to
the small size, complex shapes, and non-metallic construction material of
UAVs, and their ability to fly close to terrain. Moreover, their simple design
and ease of manufacturing from off-the-shelf components, and diverse types
have made countermeasures against UAVs challenging.

TABLE I: Comparison of the scope and topics covered in this survey with existing popular related surveys. The compared topics are provided in Fig. 2. In the table, L, M, and H are used to represent the extent of the coverage of topics, respectively, where L represents no/negligible coverage, M represents basic coverage, and H represents high coverage. Ref. |  Scope |  S1 |  S2 |  S3 |  S4 |  S5 |  S6 |  S7 |  S8 |  S9 |  S10 |  S11
---|---|---|---|---|---|---|---|---|---|---|---|---
[8] |  The future design, threats, and challenges from UAVs, and radar and non-radar methods for the detection, tracking, and classification of UAVs available in the literature are covered |  H |  M |  M |  M |  M |  L |  L |  M |  H |  L |  M
[9] |  In this survey, different detection schemes for UAVs using radar systems are provided. The main focus of the paper is to highlight the requirement for new radar systems that can detect small, slow, and low-flying UAVs |  M |  M |  H |  L |  L |  L |  L |  L |  M |  L |  L
[10] |  The survey covers different cyber and physical threats from UAVs. Popular radar and non-radar methods for the detection, tracking, and interdiction of UAVs are also covered |  H |  M |  M |  H |  M |  L |  L |  M |  M |  L |  M
[11] |  This survey focuses on different methods for the detection and classification of UAVs and birds |  M |  M |  H |  M |  H |  L |  L |  L |  H |  L |  L
[12] |  In this survey state-of-the-art methods for the detection of UAVs are provided. Moreover, different interdiction methods for countering non-cooperative UAVs are discussed |  H |  M |  M |  H |  H |  L |  M |  M |  H |  L |  M
[13] |  In this survey detection, tracking, classification, and disabling of malicious UAVs using different sensors placed at the ground and aerial platforms is discussed. A discussion of the current and future market growth of UAVs is also provided |  H |  M |  M |  M |  H |  L |  L |  M |  H |  L |  H
[14] |  State-of-the-art research for the DCT-U using different sensors and machine learning algorithms is provided in this survey |  M |  M |  M |  M |  M |  L |  L |  M |  M |  L |  M
[15] |  In this survey, different detection and defense techniques against UAVs using RF-based systems implemented using SDR are provided |  M |  M |  M |  M |  M |  L |  L |  H |  M |  L |  M
[16] |  This survey provides joint communication and sensing challenges and solutions employing mobile networks |  L |  H |  M |  L |  L |  H |  L |  L |  M |  M |  L
[17] |  In this survey different signal processing techniques for JC&S are discussed |  L |  H |  M |  L |  L |  H |  L |  L |  L |  L |  L
[18] |  In this survey, use of LTE as a source of illumination for passive radars and corresponding applications are provided |  L |  L |  L |  L |  L |  L |  H |  L |  L |  L |  L
[19] |  Detection and tracking of UAVs using passive DVB-T radar is discussed in this survey |  M |  L |  L |  L |  L |  L |  H |  L |  L |  L |  L
[20] |  In this survey detection and classification of UAVs using RF-based techniques and machine learning are discussed |  M |  L |  L |  L |  L |  L |  H |  L |  L |  L |  L
Our survey |  In this work, we have provided a comprehensive survey on the capabilities of UAVs, threats and challenges from malicious UAVs, DCT-U using radar, communication systems, RF analyzers, and non-RF systems. The limitations of radar, communication systems, RF analyzers, and non-RF systems for DCT-U are also provided |  H |  H |  H |  H |  H |  H |  H |  H |  H |  H |  H
Figure 2: Topics discussed in existing related surveys for comparison with our
survey.

The UAVs used by amateur users may also introduce major threats if their users
do not follow regulatory rules [21, 22, 23]. With all these, it is critically
important to find ways to counter malicious UAVs as well as unintended threats
from amateur UAVs [8, 23]. Fig. 1 shows a basic UAV detection scenario using a
pulse radar. The different types of clutter that can affect the detection and
tracking of the UAV are also shown. There are numerous research efforts
carried out in academia and industry to counter the threats and challenges of
malicious UAVs. According to a NATO Review Report [24], the global share of
UAV countering technologies is expected at $\$6.6$ billion by $2024$.

Figure 3: Three RF-based methods for DCT-U provided in the survey. The details
of DCT-U using the three methods are provided in the corresponding sections.
Furthermore, a brief introduction of non-RF systems and their limitations for
DCT-U is provided in Section III-D.

There are different methods available in the literature for the detection,
tracking, and classification of malicious UAVs. Popular methods include radar
systems, electro-optical/infra-red (EO/IR) imaging, radio frequency (RF)
analysis, and sound/noise analysis of UAVs [8]. Radar systems are dominantly
used compared to other techniques [25, 26, 8]. However, the probability of
miss detection for UAVs using radar systems is high mainly due to the small
radar cross section (RCS), RCS similarity to RCS of birds, and high
maneuverability close to the terrain. Moreover, as UAVs are used for different
applications and recreational purposes without a centralized traffic
management and monitoring system, it is sometimes difficult to learn about the
intent of the UAV flight.

Alternatively, or as a complementary approach, communication systems that have
generally good coverage in densely populated urban areas can be used for
DCT-U. Joint communications and sensing (JC&S) systems generally based on
mobile communication networks can be used for communications and target
detection in active mode efficiently utilizing the available spectrum [16].
Moreover, the transmissions from popular communication systems, e.g., long-
term evolution (LTE) and frequency modulation (FM) broadcasts can be analyzed
passively to detect, track, and classify UAVs [27]. DCT-U can also be
performed by passively analyzing communication signals of opportunity emitted
from UAVs using RF anlyzers [28].

###  I-A Existing Surveys and Reviews in the Literature

There are studies available in the literature that provide a review of efforts
for DCT-U. Table I provides a comparison of our survey with related existing
surveys. The scope and topics covered in this survey compared to existing
surveys are highlighted in Table I. In [8], threats and countermeasures
against malicious UAVs were covered. The countermeasures included radar and
non-radar methods. In [9], a review was provided for the detection and
classification of small UAVs using radars. For the efficient detection of
small UAVs, portable, highly mobile, inexpensive, and highly efficient radar
systems were suggested. The technical requirements and implementation details
were also provided in the paper. In [10], a survey on cyber and physical
threats from UAVs was covered. Different methods were also reviewed in the
survey for DCT-U. In [11], recent advances in the detection and classification
of rotary wing UAVs and birds using radar systems in complex scenarios were
discussed. The study covered echo modeling, fretting characteristics
recognition, the fusion of distributed multi-view features for UAV detection,
and classification using deep learning.

Figure 4: Overall structure of this survey paper. TABLE II: Abbreviations used in this paper. Acronym |  Text |  Acronym |  Text
---|---|---|---
AESA |  active electronically scanned array |  MTI |  moving target indicator
AAM |  advanced air mobility |  MMPAR |  multifunction multibeam phased array radar
AM |  amplitude modulation |  MIMO |  multiple input multiple output
AI |  artificial intelligence |  MUSIC |  multiple signal classification
ADS-B |  automatic dependent surveillance-broadcast protocol |  NLOS |  non-LOS
BS |  base station |  OFDMA |  orthogonal frequency-division multiple access
CIR |  channel impulse response |  OFDM |  orthogonal frequency-division multiplexing
CSI |  channel state information |  OTFS |  orthogonal time frequency space
CFAR |  constant false alarm rate |  PCL |  passive coherent location
CW |  continuous wave |  PESA |  passive electronically scanned array
CNN |  convolutional neural network |  PDF |  probability density function
DARPA |  Defense Advanced Research Projects Agency |  PFA |  probability of false alarm
DCT-U |  detection, classification, and tracking of UAVs |  PN |  pseudorandom
DAB |  Digital audio broadcast |  PRF |  pulse repetition frequency
DVB-S |  Digital Video Broadcast satellite |  PRI |  pulse repetition interval
DVB-T |  Digital Video Broadcast terrestrial |  PW |  pulse width
ECCM |  electronic countercountermeasures |  RATR |  radar automatic target recognition
ECM |  electronic countermeasures |  RCS |  radar cross section
EO/IR |  electro-optical/infrared |  RF |  radio frequency
FFT |  fast Fourier transform |  RX |  receiver
FAA |  Federal Aviation Administration |  SOO |  signals of opportunity
FSR |  forward scattering radar |  SNR |  signal-to-noise ratio
FM |  frequency modulation |  SDR |  software-defined radio
FMCW |  frequency-modulated continuous wave |  SVM |  support vector machine
GSM |  global system for mobile communication |  SAR |  synthetic aperture radar
GBM |  grading boosting method |  TX |  transmitter
GS |  ground station |  UHF |  ultra high frequency
HRRP |  high range resolution profile |  UWB |  ultra-wideband
IoT |  Internet of things |  USRP |  universal software radio peripheral
JC&S |  joint communications and sensing |  UAV |  unmanned aerial vehicle
LOS |  line-of-sight |  UTM |  unmanned aircraft system traffic management
LTE |  long-term evolution |  VHF |  very high frequency

Counter-UAV methods and systems were studied in [12], which included
discussions on techniques such as radar, data fusion, passive RF, acoustic,
vision, and jamming. The challenges in countering UAVs and future trends were
also covered in the survey. In [29], different approaches using radar systems
for the detection and localization of UAVs in academia and industry were
provided. The hardware and software methods and commonly used localization
algorithms were also discussed in the survey. In [13], detailed information
was provided on counter-UAV systems. The counter-UAV systems were divided into
ground and aerial platforms. The ground platform-based counter-UAV system
consisted of mobile, static, and human-packable sensors, whereas, the aerial
platform consisted of low and high-altitude sensors.

Several surveys have explored the use of communication systems for DCT of
various targets, including UAVs. JC&S approaches were investigated in previous
surveys [16, 17]. Additionally, in [18, 19], comprehensive reviews were
presented for the utilization of communication systems as a means of
illuminating UAVs for passive DCT. In [20], a review study on DCT-U based on
RF SOO emitted by UAVs was provided. Table I provides a comparison of the
topics covered and the scope of the existing literature in survey papers,
contrasting it with the contents of this survey. The numerical values, ranging
from 0 to 2 in Table I, signify the extent of coverage for each respective
topic, and letters A to H represent the topics covered.

###  I-B Contributions and Organization of This Survey

Comparing our work with the available literature, we have provided a
comprehensive survey on the capabilities and potential threats from UAVs and
different methods for DCT-U. We have provided three RF-based methods for
DCT-U. These three RF-based methods include radar systems, communication
systems, and RF analyzers. The three RF-based methods for DCT-U and their
corresponding sections in the paper are highlighted in Fig. 3. Non-RF methods
for DCT-U based on acoustic, EO/IR, lidar, and sensor fusion are also briefly
covered in this survey. Furthermore, the limitations of RF and non-RF methods
for DCT-U and possible future trends are also discussed. To the best of the
knowledge of the authors, there is no detailed survey available in the
literature that provides comprehensive details on the threats from UAVs and
challenges in the DCT-U and different methods (RF and non-RF) to counter
malicious UAVs and it is our goal to fill this gap. Table I provides a
comparison of our survey with already available related surveys.

The rest of the paper is organized as follows. Section II discusses the basics
of radar systems for DCT-U. The existing and future aerial threats and
challenging features of UAVs that make it difficult to detect and track them
are provided in Section III. Section IV reviews different types of
conventional and modern radar systems for DCT-U, Section V covers
communication systems used for DCT-U, Section VI discusses different RF
analyzers that passively capture RF SOO radiated from UAVs for DCT-U, the
future directions based on the lessons learned are provided in Section VII,
and finally, Section VIII concludes the paper. The complete structure of the
paper is outlined in Fig. 4 and the abbreviations used in the paper are
provided in Table II.

##  II Radio waves and Radar Basics

In this section, the basics of radio wave propagation and radar systems
including the interaction of radio waves with aerial vehicles and
surroundings, radar parameters, and associated signal processing techniques
are discussed.

(a)

(b)

(c)

(d)

Figure 5: The RCS of four different UAVs measured at 15 GHz and 25 GHz using
vertical-vertical and horizontal-horizontal polarization (regenerated from
[30]). The measured RCS (in dBsm) is plotted against the azimuth angle for the
range [0∘ 360∘]. The four UAVs are (a) DJI Matrice 600 Pro, (b) DJI Matrice
100, (c) Trimble zx5, and (d) DJI Mavic Pro 1.

###  II-A Radio Waves Interaction with the Aerial Vehicles and Surroundings

A simple radar system is based on capturing the reflected radio waves from a
desired object. The detection of an aerial vehicle using radio waves depends
on the physical and motion characteristics of the target and surrounding
environment. The physical characteristics of an aerial vehicle are estimated
using the RCS of the aerial vehicle by the radar. The RCS of an aerial vehicle
$\sigma$ is given as

$\sigma=\lim_{R\to\infty}4\pi R^{2}\frac{|E_{\rm s}|^{2}}{|E_{\rm i}|^{2}},$
(1)

where $R$ is the slant range between radar and aerial vehicle, while $E_{\rm
i}$ and $E_{\rm s}$ are the incident and scattered electric fields from an
aerial vehicle, respectively. The RCS of an aerial vehicle depends on the
frequency, polarization, angle of illumination, geometry, and electrical
properties of the material of the aerial vehicle. The RCS can also be used for
classification. In [30], RCS of six different types of UAVs measured at $15$
GHz and $25$ GHz were used for classification. The RCS of four UAVs measured
at $15$ GHz and $25$ GHz and at respective polarization pairs of vertical-
vertical and horizontal-horizontal are provided in Fig. 5.

The motion characteristics of an aerial vehicle include velocity, pitch, yaw,
roll angles, and rate of climb. The major motion characteristic is velocity.
The velocity of an aerial vehicle is generally obtained by measuring the
Doppler shift in frequency of the received radio signal given as $f_{\rm
d}=\frac{2v\cos\alpha}{\lambda}$, where $v$ is the velocity of the aerial
vehicle, $\alpha$ is the angle between the radar’s line of sight towards the
aerial vehicle and direction of travel of the aerial vehicle, and $\lambda$ is
the wavelength. In addition to the main Doppler shift $f_{\rm d}$, there are
additional Doppler shifts due to the motion/rotation of sub-parts of the
aerial vehicle. Such additional Doppler shifts at the micro level are
categorized as micro-Doppler, which is a helpful feature often used in the
classification of an aerial vehicle [31].

The collection of radio wave reflections (specular and diffuse) from objects
that are not of interest such as buildings and other man-made structures,
birds, trees, and hills constitute radar clutter [32]. The radar clutter can
be static or dynamic. A major source of static clutter is the ground/sea
surface reflection [33]. Dynamic clutter can be from moving vehicles/objects
in the environment, e.g., moving cars, flying planes, the rotation of wind
turbines, and the movement of foliage. The clutter is related to the angular
and range resolution of a radar system. If the angular resolution is small
(i.e., large beamwidth) large clutter is observed and vice versa. If the range
resolution is high, the clutter can be differentiated from the aerial vehicle,
and the signal-to-clutter ratio increases. The clutter cross-section can be
thousands of times larger than the cross-section of the aerial vehicle
observed by radar.

The propagation of radio waves also depends on the terrain and atmospheric
effects. The propagation effects (reflection, diffraction, and scattering) on
radio waves will be different in different terrains. Therefore, a radar system
working in a rural terrain will require calibration before it can operate in
an urban terrain. The radio waves also suffer propagation losses due to
atmospheric absorption in addition to free space loss [34]. Atmospheric
conditions, e.g., rain, hail, snow, and upper atmospheric conditions can
directly affect the detection by radar [35]. The rain, hail, snow, fog, and
other precipitation conditions can result in echoes from these particles that
superimpose the target echoes. The atmospheric effects, e.g., wind in a forest
area or strong air currents at sea can also affect the propagation
characteristics of radio waves [36, 37].

###  II-B Radar Parameters

There are different radar parameters that can affect DCT-U. Major radar
parameters are transmit power, sounding signal, pulse width (PW) and duty
cycle, pulse repetition interval (PRI), frequency and bandwidth, modulation
and coding, intrapulse modulation, and antenna parameters. The transmit power
of radars can vary from a few milliwatts to megawatts. The transmit power is
mainly dependent on the application and is constrained by the platform. The
high transmit power allows a greater range for radar systems by overcoming
free space attenuation. To detect UAVs with small RCS at long ranges, large
transmit power is often required.

Figure 6: Different types of radar waveforms.

Different signal waveforms used by radar systems are provided in Fig. 6.
Mainly the waveforms are divided into pulse and continuous wave (CW)
waveforms. Pulse and CW waveforms are further divided into other waveform
types. Each waveform in Fig. 6 has specific use and advantage. Overall, CW
radar systems are simpler compared to pulse radar systems. However, pulse
radar systems have a high dynamic range due to the isolation of transmitter
(TX) and receiver (RX). The high dynamic range allows long-range detection
capability. Furthermore, continuous high-energy transmissions are not possible
from a single TX/RX antenna (monostatic radar) and it can damage the RX if not
stopped by a Duplexer. Therefore, a listening time is required for pulse
radars. The listening time is constrained by the duty cycle, PW, and PRI.

Figure 7: An RF pulse train showing the PW, PRI, and pulse peak and average
power.

The peak power, average power, PW, PRI, pulse repetition frequency (PRF), and
duty cycle for a radar pulse train shown in Fig. 7 are related to each other
as follows [38]:

$\rm{duty~{}cycle}=P_{\rm avg}/P_{\rm p}=\rm{\gamma}/\rm{T_{\rm
r}}=\rm{\gamma}\times\rm{f_{\rm r}},$ (2)

where $P_{\rm avg}$ is the average power, $P_{\rm p}$ is the peak power,
$\gamma$ is the PW, $T_{\rm r}$ is the PRI, and $f_{\rm r}$ is the PRF. The
parameters of short and long pulse radar test waveforms are provided in Table
III, and Table IV, respectively. The design of both short and long pulse radar
test waveforms in Table III, and Table IV, respectively, are based on the
compliance measurement procedure outlined by the FCC for unlicensed devices.
Noticeably, the short pulse waveforms have a higher number of pulses per burst
compared to long pulse waveforms, whereas, the PW of short pulse waveforms is
significantly smaller than the long pulse waveforms as expected. The minimum
percentage of successful detection and PRI is higher for long-pulse waveforms
compared to short-pulse waveforms.

TABLE III: Short pulse radar test waveform (adapted from [39]). Type of Radar |  PW ($\mu$s) |  PRI ($\mu$s) |  Number of pulses |  Minimum percentage of successful detection |  Minimum number of trials
---|---|---|---|---|---
0 |  1 |  1428 |  18 |  See note 1 in [39] |  See note 1 in [39]
1 |  1 |  Test A, and Test B, in [39] |  $\rm{Roundup}\Big{(}\big{(}\frac{1}{360}\big{)}.\big{(}\frac{19.10^{6}}{PRI}\big{)}\Big{)}$ |  60% |  30
2 |  1-5 |  150-230 |  23-29 |  60% |  30
3 |  6-10 |  200-500 |  16-18 |  60% |  30
4 |  11-20 |  200-500 |  12-16 |  60% |  30
TABLE IV: Long pulse radar test waveform (adapted from [39]). Type of radar |  PW ($\mu$s) |  Chirp width (MHz) |  PRI ($\mu$s) |  Number of pulses per burst |  Number of bursts |  Minimum percentage of successful detection |  Minimum number of trials
---|---|---|---|---|---|---|---
5 |  50-100 |  5-20 |  1000-2000 |  1-3 |  8-20 |  80% |  30

The majority of the radar systems operate between $400$ MHz to $36$ GHz
frequency range. The use of a particular frequency band depends on 1) the role
of the radar, i.e., search, tracking, or guidance; 2) the type of aerial
vehicles expected to detect and track; 3) the terrain; 4) the platform over
which radar is mounted; and 5) range of the radar. The long-range search radar
uses low frequencies generally VHF and UHF bands. The low frequencies allow
long-range detection capabilities. The close-range tracking and guidance radar
systems require higher resolution and narrow beams compared to search radars
and therefore use higher frequencies compared to search radars. The different
frequency bands used by radar systems and corresponding applications are
provided in Table V. Major applications of high-frequency bands are close-
range targeting, tracking, and high-resolution imaging, e.g., for small RCS
UAVs, whereas, low-frequency bands are mainly used for long-range target
detection.

The bandwidth of a radar is an important parameter and determines the range
resolution of the target. The bandwidth of a pulse radar is equal to the
reciprocal of the transmitted PW. The range resolution of a radar is dependent
on the bandwidth or PW. The greater the bandwidth, the better the range
resolution. The bandwidth and the signal power (or range) are exchangeable due
to the dependence of both on the PW. Ultra-wideband (UWB) radars are popular
due to their large bandwidth (high range resolution) [40]. The multipath
components from a UWB radar can be resolved in the order of centimeters,
therefore, fine details of the aerial vehicle or image can be obtained. The
large bandwidth is also helpful in distinguishing an aerial vehicle from the
clutter.

Different types of modulations are used by different types of radar systems
[41]. Modulation types for the CW and pulse radar are shown in Fig. 6. In
addition to basic pulse modulations, pulse radar can use phase and frequency
intrapulse modulations. Similarly, frequency-modulated continuous wave (FMCW)
radar can have different types of phase and frequency intrapulse modulations
as shown in Fig. 6. The major benefit of intra-pulse modulations is higher
range resolution without compromising the signal-to-noise ratio (SNR) [42].
The phase intrapulse modulations are further divided into bi-phase and poly-
phase modulations whereas the frequency intrapulse modulations are further
divided into linear and non-linear FM and frequency shift keying. The bi-phase
and poly-phase intrapulse modulations use different codes shown in Fig. 6.
Sequences of numbers of different lengths are used in these codes. Other radar
coding schemes are provided in [43, 44].

TABLE V: Different frequency bands for radars and their applications. Radar band |  Frequency (GHz) |  Popular applications
---|---|---
Millimeter |  40-100 |  UAV detection, tracking, and navigation, airborne radar, spaceborne radar, SAR
Ka |  26.5–40 |  Airborne close range targeting, airport surveillance, traffic speed detection, SAR
K |  18–26.5 |  Small UAV detection, airborne close range targeting, traffic speed detection
Ku |  12.5–18 |  High resolution mapping, satellite altimetry, air-traffic control, air-borne radar
X |  8–12.5 |  Short range tracking, guidance, UAV detection and tracking, marine radar, air-traffic control
C |  4–8 |  Long range tracking, weather monitoring, SAR
S |  2–4 |  Moderate range surveillance, air-traffic control, weather monitoring, surface ship radar
L |  1–2 |  Long range surveillance, small UAV detection, atmospheric studies, air-traffic control, SAR
UHF |  0.3–1 |  Very long range early warning against aerial threats, anti-stealth
VHF |  0.03 to 0.3 |  Very long range early warning against aerial threats, anti-stealth

Directional antennas are mainly used by radar systems for the detection and
tracking of aerial vehicles. The high gain (and narrow beamwidth in the
azimuth and elevation planes) from a directional antenna helps to achieve
long-range (see (3)), fine clutter rejection and high angular resolution. In
addition, the position of the narrow beamwidth directional beam can be used to
roughly estimate the position of the aerial vehicle. Popular radar antennas
were simple parabolic reflectors and Cassegrain feed parabolic reflectors that
were mechanically rotated. Nowadays, electronically scanned phased arrays are
used that require minimum mechanical assembly for rotation. The electronically
scanned phased arrays are further divided into passive and active types [32].
Active electronically scanned array (AESA) is commonly used in modern radar
systems. Analog, digital, or hybrid beamforming can be used in AESA [45].

###  II-C Popular Radar Signal Processing Techniques

The majority of the signal processing techniques are similar for different
types of radar systems. The main signal processing takes place at the radar
RX. The common signal processing units at the radar RX include an analog-to-
digital converter, low-noise amplifier, frequency mixer and filter, matched
filter, and threshold detector. The maximum detection range of a commonly used
radar is given as

$R_{\rm max}=\bigg{(}{\frac{P_{\rm T}G^{2}\lambda^{2}\sigma}{P_{\rm
R,min}(4\pi)^{3}L}}\bigg{)}^{\frac{1}{4}},$ (3)

where $R_{\rm max}$ is the maximum range for the radar, $P_{\rm T}$ is the
transmit power, $G$ is the antenna gain, $P_{\rm R,min}$ is the minimum
received power, and $L$ represent the total losses. The detection of multiple
targets using a commonly used pulse radar is challenging due to range
ambiguity. Radar-based signal processing techniques, for example, provided in
[46, 47, 48] can be used for the detection and tracking of multiple targets
simultaneously.

Generally, a target is tracked using its position and velocity information.
The track equation for radar is given as

$S/N=\frac{P_{\rm T}G^{2}\lambda^{2}\sigma}{(4\pi)^{3}R^{4}kT_{\rm s}B_{\rm
n}L},$ (4)

where $S/N$ is the SNR, $k$ is the Boltzman constant, $T_{\rm s}$ is the
system noise temperature, and $B_{\rm n}$ is the noise bandwidth of the RX.
The average noise power is represented by $kT_{\rm s}B_{\rm n}$. There are
different tracking algorithms available in the literature. In [49] FMCW radar
and MUSIC algorithm were used for multiple UAV tracking. In particular, two
different UAVs were tracked simultaneously in an open area using the MUSIC
algorithm at low altitudes. In [50], high-resolution radar and Hough transform
were used for tracking small UAVs. Hough transform provided a steady and
continuous tracking of UAV using a linearly distributed micro-Doppler
signature. The Hough transform helped to resolve the problem of scatterer
clustering. Moreover, there are different methods available in the literature
for the localization of a moving object based on target tracks [51, 52, 53].
Kalman and particle filters are also popular for the localization and tracking
of mobile nodes with radars [54].

In a pulse radar system, the coherent integration of received pulses is used
to increase the SNR. The coherent integration requires both the in-phase and
quadrature components of the received signal. Furthermore, compressed sensing
can be used for sparse radar signals and a reduced number of sample scenarios.
For example in [55, 56], compressed sensing was used in the reconstruction of
the sparse target scene that helped in the detection of the target in complex
scenarios. Other radar estimation problems, e.g., finding the high-resolution
direction of arrival of signals from multiple sources at a single snapshot
were obtained through compressed sensing techniques in [57].

Doppler processing is essential for estimating the velocity of an aerial
vehicle in all the radar systems. The frequency shift of the received signal
from the center frequency is used to obtain the Doppler shift and subsequently
the velocity estimate of the aerial vehicle. Micro-Doppler processing can also
be used to obtain the micro-Doppler signature of a moving aerial vehicle. In
older radar systems, RCS and the velocity of an aerial vehicle were used for
classification. However, nowadays, modern classification algorithms use
additional target features for classification, e.g., micro-Doppler signature.
The classification is also aided by AI algorithms. Deep learning AI algorithms
help to accurately identify the type of the aerial vehicle [58, 59].

##  III Analysis of Aerial Threats and Challenges

In this section, the capabilities of UAVs, current and future aerial threats
and challenges from UAVs, and limitations of radar systems in DCT-U are
provided. A brief discussion of non-RF systems and their limitations in the
DCT-U are also provided.

TABLE VI: Common features and specifications of different types of UAVs. In the table, $L$, $W$, and $H$ represent the length, width, and height, respectively. The provided data is taken from different internet sources. | Specifications of different types of UAVs
---|---
UAV type |  Single/multi-rotor |  Flat/tilt wing |  Hot air balloons |  Satellites
Max. Size ($L$$\times$$W$$\times$$H$) |  1m$\times$1m$\times$0.63m |  14m$\times$1.7m$\times$4m |  17m$\times$17m$\times$24.5m |  73m$\times$109m$\times$20m
Airframe material |  Carbon fiber |  Carbon fiber |  Nylon, polyester |  Aluminum alloys
Max. flight Endurance |  3 hours |  42 hours |  475 hours |  15 years
Max. payload capacity |  8 kg |  100 kg |  600 kg |  29,000 kg
Max. flight ceiling |  6 km |  18 km |  21 km |  35786 km
Max. speed |  29 m/s |  130 m/s |  4.47 m/s |  3138.9 m/s
Propulsion System |  Propeller |  Propeller |  NA |  Chemical thrusters
Operating frequencies |  900 MHz - 5.8 GHz |  900 MHz - 5.8 GHz |  123.3 - 123.5 MHz |  L, C, X, Ku, Ka band
Navigation |  Internal/external |  Internal/external |  NA |  Internal/external
Heat signature |  Small |  Very small |  Large |  Small
RCS |  Small |  Small/medium |  Large |  Large

###  III-A Capabilities and Features of Common UAVs

There are different types of UAVs available. Table VI shows the major types
and specifications of typical UAVs. In Table VI maximum values of size, flight
endurance, payload capacity, flight ceiling, and speed for UAVs sorted from
the internet are provided. From Table VI it can be observed that the UAVs have
in general small size, weight, power consumption, heat signature, and RCS
compared to manned aerial vehicles. Moreover, fixed-wing and rotary-wing UAVs
offer medium flight endurance, payload capacity, flight ceiling, and speed.
The cost of UAVs is also significantly small and can be easily produced in
large numbers using simple design and readily available off-the-shelf
components. The simple design allows various sizes of UAVs. For example, a UAV
can fit into the palm of a hand to a wing span of more than $43$ m. The
capabilities of UAVs are summarized in Fig. 8.

###  III-B Threats and Challenges from UAVs

UAVs operated by amateur users who do not follow regulations pose a
significant threat to the surroundings. Moreover, UAVs can be used by non-
state actors for malicious purposes. Examples of major current and future
threats from malicious UAVs can be listed as follows: 1) use by non-state
actors during conflicts; 2) conventional, biological, and chemical threats
carried by UAVs; 3) threats to sensitive infrastructure, e.g., dams, bridges,
important buildings, power generation plants, chemical, and nuclear
facilities; 4) threats to important persons, vehicles, and locations; 5)
threats to crowded areas; 6) malicious activities e.g., starting a fire, and
identity theft; 7) smuggling of contraband articles using UAVs; 8) threats to
the aviation industry; 9) planting improvised explosive devices, and mines on
ground and at sea; 10) hacking UAVs and flying them for malicious purposes;
11) eavesdropping; 12) ECM using UAVs; 13) use as decoys; 14) different types
of UAVs flying in a swarm; and 15) threats from high-altitude flying
platforms, e.g. hot-air balloons that can fly at high altitudes and carry
large payloads.

Figure 8: The capabilities of UAVs and major challenges in DCT-U due to
characteristics of the UAVs.

There are many challenges to the accurate and timely DCT-U. The major such
challenges due to the unique capabilities of UAVs are summarized in Fig. 8 and
given as follows:

  * •

Multiple types and shapes of UAVs are difficult to classify.

  * •

UAVs flying in a discontinuous flight path are difficult to detect and track.
For example, multiple landings during the flight of a UAV at random intervals
can pose challenges to detection and tracking.

  * •

Hybrid UAVs that can fly and move over land and water can achieve long
operational ranges.

  * •

Modular design and changes during the flight of UAVs, e.g., the continuous
disintegration of UAVs into multiple autonomous UAVs can make detection,
tracking, and classification challenging.

  * •

Smart structural modifications of UAVs using AI and 3D printing during flight
can make DCT-U difficult.

  * •

UAVs can use AI to make autonomous run-time decisions based on the
environment, clutter, and RF emissions in the environment and find the optimum
route. AI-controlled autonomous UAVs have better situational awareness and are
difficult to detect and track. Moreover, trajectory control of UAVs using
multiple sources, e.g., onboard smartphones, and navigation using
telecommunication coverage can make the UAV resilient to different RF
countermeasures.

  * •

Another major challenge is the classification of small, low-flying UAVs from
birds and the identification of malicious UAVs from hobbyist UAVs.

  * •

UAVs flying in a swarm are difficult to detect, track, and classify [9, 60,
61] especially when they fly at different altitudes and at different
velocities in a specific trajectory are difficult to detect, track, and
classify.

  * •

Decoy UAVs that exist either physically or created virtually using ECM
techniques for deception in a swarm can result in the wastage of resources on
their detection and tracking.

  * •

UAVs can be produced in large numbers due to simple design and off-the-shelf
readily available components. On the flip side, effectively countering UAVs
necessitates highly sophisticated systems. However, it’s important to note
that these advanced counter-UAV systems come with a considerable financial
burden, making them expensive to acquire and manage.

  * •

In a complex environment, e.g., in a dense urban area with high-rise buildings
and dynamic clutter (from civilian planes, cars, pedestrians, and birds), the
detection and tracking of small UAVs becomes challenging.

TABLE VII: RCS of different types of UAVs using different types of radar systems. UAV type |  RCS |  Center frequency |  Sounding signal |  Ref.
---|---|---|---|---
DJI Matrice 600 Pro, DJI Matrice 100, Trimble zx5, DJI Mavic Pro 1, DJI Inspire 1 Pro, DJI Phantom 4 Pro |  Lognormal, generalized extreme value, and gamma distributions |  15 GHz, 25 GHz |  CW |  [62]
DJI Phantom 4 Pro |  0.01 m2 |  25 GHz |  FMCW |  [63]
DJI Phantom 3 |  0.01 m2 |  Ku band |  Pulse based phased array radar |  [64]
Mavic Pro |  0.03 m2 |  2.4 GHz |  CW, linear frequency modulated |  [65]
Iris+, X8, and High one |  See Table II in [66] |  3 GHz, 9.7 GHz, 15 GHz, and 24.3 GHz |  Pulse Ku-band short range battlefield radar |  [66]
Phantom 3, fixed-wing buzzard |  Maximum value 0.09 m2 |  X-band |  FMCW |  [67]
UAV |  0.08 m2 |  S-band |  Pulses |  [68]
TABLE VIII: Comparison of different sensors over a range of performance indicators (adapted from [69]). |  Long range radar with micro-Doppler |  Short range high frequency radar with micro-Doppler |  Airborne short range radar |  RF triangulation |  Clustered audio sensors |  EO/IR sensor |  Airborne EO/IR sensor
---|---|---|---|---|---|---|---
Range |  Extreme |  Medium |  Medium |  Good |  Short |  Medium |  Medium
Dynamic environment coverage |  Bad |  Bad |  Good |  Good |  Good |  Bad |  Good
Detection in urban environment |  Medium |  Medium |  Medium |  Medium |  Medium |  Bad |  Good
Detection in darkness |  Good |  Good |  Good |  Good |  Good |  Good |  Good
Distance to target |  Good |  Good |  Good |  Good |  Bad |  Bad |  Bad
Speed of target |  Good |  Good |  Good |  Good |  Bad |  Bad |  Good
Mobility |  Bad |  Medium |  Good |  Bad |  Medium |  Good |  Good
Detection in fog |  Good |  Good |  Good |  Good |  Good |  Bad |  Bad
Detection in rain |  Good |  Good |  Medium |  Good |  Medium |  Medium |  Medium
Weight |  High |  Medium |  Low |  High |  Medium |  Low |  Low
Rogue UAV detection |  Yes |  Yes |  Yes |  No |  Yes |  Yes |  Yes
UAV swarm detection |  Medium |  Medium |  Good |  Good |  Good |  Good |  Good
Processing power |  High |  High |  High |  High |  Low |  Medium |  Medium
Power consumption |  High |  Low |  Low |  Medium |  Medium |  Low |  Low
Low speed UAV detection |  Good |  Good |  Good |  Good |  Good |  Good |  Good
Low cost |  Bad |  Medium |  Medium |  Medium |  Medium |  Good |  Good
Maintenance |  Good |  Good |  Bad |  Good |  Medium |  Medium |  Bad
Installation ease |  Bad |  Medium |  Good |  Medium |  Medium |  Medium |  Good

###  III-C Limitations of Radar Systems

DCT-U of typical UAVs is challenging due to their unique capabilities
discussed earlier. While radar is the most popular sensor for DCT-U, there are
some common limitations of radar systems listed as follows:

  * •

In general, radar systems are inefficient because a large amount of energy is
transmitted, whereas, only a fraction of the transmitted energy is received
back from clutter and from a potential aerial vehicle. Also, the RF energy
emissions from radar systems expose them to detection.

  * •

The commonly available small UAVs have small RCS. In addition to the small RCS
of UAVs, if radiation-absorbent material and specific shapes are used, the RCS
of the UAVs can be further reduced. Small RCS UAVs are difficult to detect by
common radar systems at long ranges and cluttered surroundings. The RCS of
different types of UAVs reported in the literature at different frequencies
are overviewed in Table VII. From Table VII, it can be observed that the
majority of RCS measurements were carried out at frequency bands $>2~{}$GHz
and the RCS of the majority of commercially available UAVs is less than $0.1$
m2.

  * •

Extreme flight maneuvers are possible with UAVs due to the absence of a pilot.
For example, UAVs can fly very close to the terrain to avoid detection by
radar systems.

  * •

The range and Doppler ambiguities in popular pulse-Doppler radars put bounds
on the range and velocity measurements of the target. The range and Doppler
ambiguities become notable when detecting multiple UAVs at different ranges
and velocities, e.g., in a swarm.

  * •

The DCT-U of multiple small UAVs in a swarm requires high angular and range
resolution radar. However, the scan duration and complexity of the radar
system increase with the increase of the range and angular resolution.

  * •

The detection of small and low-flying UAVs in highly cluttered environments
requires adaptive adjustments of radar thresholds in order to keep the PFA
low.

  * •

The typical radar systems have limitations in tracking very high altitude,
highly maneuverable, and high-speed UAVs.

  * •

The radar systems are vulnerable to ECM applied by UAVs and require electronic
counter-countermeasures (ECCM) against ECM. Popular offensive ECM that can be
applied by UAVs include jamming hostile radio signals, spoofing the radio
signals with false data [70], or transmitting multiple copies of the echo
signals as decoys. The ECCM includes robust frequency hopping over broadband,
multilayered authentication and encryption of data, detection, and reporting
of jammed strobe, and dedicated RX for monitoring of the ECM provided in [71,
72].

  * •

Noise, RF interferences, and clutter affect the performance of a radar system.

###  III-D Non-RF Systems and their Limitations

The popular non-RF systems for DCT-U are EO/IR and acoustic. A brief
introduction and limitations of these non-RF systems for DCT-U are as follows:

  * •

Passive imaging sensors such as EO/IR can be used for DCT-U with high
precision. In [73], a review of different algorithms used for real-time
detection of UAVs using EO sensors was provided. A thermal infrared camera is
used for the nighttime detection of UAVs in [74]. However, in an EO/IR sensor,
the complexity of the target’s background and thermal image saturation affects
the detection and classification performance of the EO/IR sensors for UAVs.
The range of the EO/IR is also limited by the horizon. The atmospheric
effects, e.g., haze, fog, rain, and snow can affect the performance of EO/IR
sensors.

  * •

Acoustic signals from a flying UAV can be detected using an acoustic sensor,
e.g., a microphone. The acoustic signals captured by a microphone can also be
used for tracking and classification of UAVs [14, 75]. A major limitation of
acoustic methods is their poor performance in noisy environments and their
range is limited. The acoustic methods work in the passive mode and a single
acoustic sensor cannot provide precise localization of the UAV. Furthermore,
the acoustic methods work poorly against high-altitude UAVs.

  * •

Sensor fusion of different sensors can be used for DCT-U. For example, EO/IR
and acoustic sensors can be used in a network. Multiple heterogeneous sensors
can use sensor fusion to overcome the weaknesses of the individual sensors and
provide combined strength [76]. However, there are limitations due to
networking and delays arising from the central data processing of different
sensors.

A comparison of radar and non-RF systems over different performance indicators
[69] is provided in Table VIII. From Table VIII, it can be observed that
short-range airborne radar has the overall best performance when detecting
UAVs. The short-range high-frequency radar with micro-Doppler also has good
performance in detecting UAVs compared to other sensors. Furthermore, some of
the limitations of the EO/IR sensor can be overcome by mounting it on the
airborne platform.

##  IV DCT-U using Conventional and Modern Radar Systems

In this section, the literature on the use of conventional and modern radar
systems for DCT-U is covered.

###  IV-A Conventional Radar Systems for DCT-U

Conventional radar systems can be categorized as radar systems that have
simple hardware, and basic signal processing and are typically used. For
example, a monostatic real aperture radar with basic signal processing is
shown earlier in the survey in Fig. 1. Pulse Doppler, CW, and FMCW radars can
also be categorized as conventional radars and are often used for DCT-U. In
[77], a pulse Doppler radar and convolutional neural network (CNN) were used
for the detection and localization of UAVs with minimum constant false alarm
rate (CFAR). A two-head CNN was used, one for the patch of range-Doppler map
classification and the other for the offset regression between the patch
center and the target. Subsequently, a non-maximum suppression was used to
keep CFAR low for small UAVs.

A conventional pulse Doppler radar and Hough transform were used for the
detection and tracking of small UAVs in [50]. The linear distributed features
of micro-Doppler and Hough transform helped to detect and identify small UAVs.
In [78], a CW radar was used for the detection of small UAVs. Two stages were
used for the detection of UAVs using CW radar. First was the extraction of UAV
parameters using the Gaussian mixture model and then trajectory extraction
using multi-frame information. In [79], an X-band radar was used for the
detection and tracking of small UAVs with RCS of $.01$ m2. The FMCW radar was
able to detect small quadcopter UAVs at a distance of $20$ m to $500$ m. The
radar was able to differentiate between two UAVs flying at a distance of $20$
m apart. FMCW radar was also used for the detection of UAVs in [80, 81, 82].

Other commonly used radars for DCT-U include marine and staring radars. In
[67] a marine broadband FMCW radar operating at X-band was used for the
detection of two different types of UAVs. Measurements were carried out to
detect rotary wing and fixed wing UAVs using the marine radar at a distance of
$400$ m. Similarly, in [83], low-cost marine radar was used for the detection
of UAVs. The marine radar was mounted on a UAV and provided $360^{\circ}$
coverage over a large area. In [84] a staring radar operating at L-band was
used to differentiate between UAVs and birds. The measurements were taken in
an urban area and the measurement data was used as training data for
classification algorithms. In [85], Aveillant Gamekeeper staring radar was
used for surveillance of non-cooperative UAVs. The radar operated at L-band
and used extended dwell that helped in small UAV detection over a range of
several kilometers. Moreover, machine learning classifiers were used to
distinguish between UAVs and birds using the $3$D radar data.

Figure 9: PESA and AESA radars are shown. AESA radars have multiple TX/RX
modules that can work independently and act as individual radars.

###  IV-B Detection and Tracking of UAVs Using Modern Radar Systems

In contrast to conventional radar systems, modern radar systems leverage
advancements in both hardware and software technologies [86]. Modern radar
systems utilize cutting-edge digital signal processing, advanced RF front-ends
and antenna technology, and machine learning. Modern radar systems can provide
detection at long ranges, maintain a low probability of intercept, adaptively
select transmission parameters based on the scenario, and process sparse and
weak signals. Moreover, modern radar systems exhibit superior clutter
rejection capabilities, high range and angular resolution, enabling the
detection and tracking of multiple targets simultaneously, enhanced
classification capabilities, a low false alarm rate, and resistance to
electronic countermeasures.

The majority of modern radar systems use electronically steered phased arrays.
Passive electronically scanned array (PESA) and AESA are two main types of
electronically steered phased-array-based radar systems shown in Fig. 9. AESA
is widely used nowadays to perform search and track tasks simultaneously [87,
88]. Multiple AESA beams are used to perform different tasks simultaneously,
e.g., communications, passive listening, and imaging, in addition to search
and track tasks. The range equation for AESA radar [89] is modified compared
to the conventional radar equation provided in (3). In particular, the
contribution of individual TX/RX modules is considered in the AESA range
equation given as

$R=\bigg{(}{\frac{N^{3}p\pi^{2}\lambda^{2}\sigma T_{\rm d}}{(4\pi)^{3}kT_{\rm
s}D_{\rm x}(n^{\prime})L_{\rm t}L_{\rm a}}}\bigg{)}^{\frac{1}{4}},$ (5)

where $N$ is the number of TX and RX modules, $p$ is the mean power of each
TX/RX module, the antenna gain at the TX and RX is $G=\pi N$ for the broadside
direction with $N$ TX/RX modules, $T_{\rm d}$ is the dwell time, $D_{\rm
x}(n^{\prime})$ is the effective detectability factor (see [89]), $L_{\rm t}$
is the transmission line loss, and $L_{\rm a}$ is the atmospheric loss.

In [90], a multifunction multibeam phased array radar (MMPAR) placed onboard a
UAV was used to detect and track multiple UAVs in a swarm simultaneously. The
MMPAR in [90] worked autonomously using reinforcement learning. The
multifunction beams used by MMPAR and malicious UAV swarm detection and
tracking scenarios using MMPAR onboard UAVs are shown in Fig. 10(a) and Fig.
10(b), respectively. The multiple radar beams are used for volume search, cued
search, tracking, communications, and passive RF listening. The overall radar
resources are shared among different radar beams at different scanning
instances. In addition to AESA radar, other modern radar systems for DCT-U are
provided in the following subsections.

(a)

(b)

Figure 10: MMPAR radar onboard UAV for DCT-U swarm (regenerated from [90]);
(a) MMPAR onboard a UAV using different types of beams simultaneously, (b) a
scenario where two MMPAR onboard two UAVs detect and track multiple UAVs
simultaneously using volume and cued search, and tracking beams. Also, the
passive RF listening beam is used for the detection of active RF transmissions
from a malicious UAV in the swarm. The communication beams between the two
UAVs provide a continuous communication link.

####  IV-B1 Bistatic and Multistatic Radars

Bistatic and multistatic radar systems can collect reflections from a UAV at
different locations and increase the detection probability for a small RCS
target. In [91], a multibeam and multiband software-defined bistatic passive
radar was used for the detection of small RCS UAVs. In [92], a multistatic
radar system was used for the detection and tracking of a micro-UAV. Micro-
Doppler signatures were used to differentiate between UAV returns and ground
clutter. The measurements from multiple bistatic pairs were fused together for
accurate tracking of the UAV.

####  IV-B2 Cognitive Radars

Cognitive radar systems can autonomously detect and track UAVs by selecting
optimum radar parameters based on the scenario [93]. In [94], a cognitive
radar system for the detection and classification of micro-UAVs was proposed.
A binarized deep belief network and micro-Doppler signatures were used for the
detection and classification of UAVs. Similar to cognitive radar systems,
adaptive radar systems can also be used for the detection and tracking of
UAVs. In [95], a phased array C-band radar operating at multiple channels was
used. The adaptive architecture helped to resolve a single UAV from multiple
UAVs in the range-Doppler and angular domains while optimally utilizing the
resources.

####  IV-B3 Software-defined Radio-based Radars

Software-defined radios (SDRs) offer many advantages, e.g., flexibility during
run-time changes, system upgrades without significant hardware changes, and
cost-effective solutions. SDRs can be used to implement different types of
radar systems for the detection and tracking of UAVs [15]. In [96], an SDR-
based FMCW radar was used for the detection and localization of UAVs. The
detection and localization of UAVs were carried out using off-the-shelf
commonly available SDRs. Similarly, in [15], low-cost SDRs were used for the
detection, tracking, jamming, and spoofing of unauthorized UAVs. Real-time
measurements were carried out to prove the effectiveness of the proposed radar
system.

TABLE IX: Tracking of UAVs by radar systems using different tracking algorithms. UAV type |  Radar/sensor type |  Tracking features |  Tracking algorithm |  Ref.
---|---|---|---|---
Motoar Sky MS-670 |  FMCW Interferometric radar |  Radial velocity |  Kalman filter |  [97]
Micro-UAV |  Multistatic radar NetRAD |  Micro-Doppler, 2D aerial vehicle state, bi-static range |  Extended Kalman filter |  [92]
Multi-rotor UAV |  Networked radar systems |  Detections from multiple radars |  Recursive random sample consensus algorithm, tuned Kalman filter |  [98]
Multi-rotor UAVs |  A dynamic radar network onboard UAVs |  State space model |  Local Bayesian estimator, extended Kalman filter |  [99]
DJI Phantom 3, Parrot Bebop 2, Parrot Disco, DJI MAVIC Pro. |  Velodyne VLP-16 lidar |  Speed, overall motion, laser scanned data |  Kalman filter |  [100]
UAVs |  Mobile radar |  3D position coordinates and velocity of the UAV |  Kalman filter variants with east-north-up improvements |  [101]
F450 and phantom 3 |  FMCW radar, acoustic and optical sensors |  Data from three sensors |  Kalman filter, single source detection algorithm, multiple drone detection algorithm, MUSIC algorithm |  [49]
Miniature rotor UAV |  Ku-band radar |  Micro-Doppler features |  Hough transform |  [50]

####  IV-B4 Interferometric Radars

Interferometric radars use the phase shift of the received radar signals at
two different points to determine the direction of arrival. In [97], an
interferometric radar was used for the detection and tracking of UAVs. The
range and angular velocity information were obtained through interferometric
radar measurements. The UAV was also tracked by predicting future positions
using range and angular velocity measurements. In [102], a phase
interferometric Doppler radar was used for the detection and tracking of UAVs.
The measurements were obtained using a dual-channel Doppler radar. Range,
azimuth position, and Doppler processing were carried out. The joint range-
Doppler-azimuth processing was used for the detection and tracking of micro-
UAVs.

####  IV-B5 MIMO Radars

Multiple-input-multiple-output (MIMO) radars operate similarly to multistatic
radars. However, instead of spatially distributed nodes as in multistatic
radars, the antenna elements are placed closely. MIMO radars provide high
spatial and Doppler resolution and dynamic range. In [103, 104], MIMO radars
were used for the detection and tracking of UAVs. In [105], small UAV
detection was carried out using a MIMO radar and sparse array optimization and
calibration method. In [106], an S-band MIMO radar was used for the detection
of small UAVs. A target-based calibration of the MIMO radar array was carried
out to improve SNR. The MIMO radar was able to detect and track Phamtom $4$
UAV at a distance of $5$ km.

####  IV-B6 Passive Radars

Passive radar systems can also be used for the detection and tracking of UAVs.
A major advantage of passive radar systems is that a dedicated emitter is not
required. The target illuminator can be any RF source. The illuminator sources
for passive radars can be GSM, LTE, $5$G, WiFi, radio, or TV signal
transmissions. Moreover, the multiple passive listener nodes at different
locations can help to collect weak scattered reflections from a UAV. The
majority of communication-based radars discussed in Section V are passive.

####  IV-B7 Compressed Sensing-based Radars

Compressed sensing helps to achieve better range-Doppler resolution compared
to traditional radar systems in cluttered and sparse radar signal reception
scenarios [56, 55]. In [107], a UWB radar with a compressed sensing-based
adaptive filter was used for UAV detection. Compressed sensing helped to
suppress the clutter and achieved a low false alarm rate. In [108],
simulations were carried out using passive radars for the detection of UAVs
using compressed sensing and the Monte Carlo method. The effect of different
factors on the detection of UAVs, e.g., antenna placement, and directivity was
also covered.

####  IV-B8 Microwave Photonic Radars

Microwave photonic radars use large bandwidth, provide resistance to microwave
interference at long ranges, and have the ability to simultaneously operate
multiple coherent signals. In [109, 110], latest trends and advances using
photonic radar technology were discussed. In [111], a photonic radar operating
in the X-band was used for the detection of small RCS UAVs. The radar used
photonic frequency for transmission. The received signal was detected using a
delay interferometer and photo-detector. The photonic radar was able to detect
small RCS UAVs at a distance of $2.7$ km. In [112], a photonic radar using
$11$ GHz bandwidth and a simple signal generation and processing setup was
used for high-resolution identification of moving propellers of a UAV.

####  IV-B9 Monopulse Radars

Monopulse radars can be used to obtain the accurate position of a UAV as well
as the detection and tracking of a UAV. Such radars use additional coding of
the RF signal to obtain accurate directional information. In [95], a multi-
channel phased array radar operating in the C-band was used for the detection
of multiple UAVs. The UAVs were closely spaced in the angular domain and were
not resolvable in the range-Doppler domain. A monopulse algorithm was used to
detect and find the azimuth position of multiple UAVs. In [113], a coordinated
radar scenario for the tracking of a UAV was presented. The UAV was localized
with the help of monopulse trackers that coordinated through a backhaul. The
monopulse trackers achieved high localization performance at a low feedback
rate.

####  IV-B10 Polarimetric Radars

Polarimetric radars can be used to classify UAVs based on polarimetric
parameters. In [114], polarimetric parameters of S-band radar were used to
distinguish between small UAVs and birds. In the time-frequency domain, the
analysis of polarimetric parameters was carried out to differentiate different
scatterers based on micro-motion that is resolved in time and velocity. The
orientation of scatterers and polarimetric signature helped in the target
classification. In [115], polarimetric analysis of UAVs was carried out from
different aspect angles, using micro-Doppler signature that helps in the
identification of UAV micromotions.

####  IV-B11 Airborne Radars

Airborne radar systems can be used for DCT-U. Airborne radar systems have the
advantage of longer detection ranges, better tracking with the help of
maneuvering, and reduced clutter compared to ground-based radar systems. In
[90], MMPAR radar was used for the detection, tracking, and classification of
multiple malicious UAVs in a swarm. The multiple radar beams were controlled
autonomously through reinforcement learning. Reinforcement learning helped to
overcome radar anomalies. In [116], active and passive radars onboard UAVs in
a swarm were used for the detection of stealth aerial vehicles. The swarm of
UAVs used a combination of active and passive radars that helped in the
detection of stealth aerial vehicles. An airborne aerial dragnet system by
DARPA is used for the detection and tracking of UAVs [117]. The aerial dragnet
system used either tethered UAVs or long-endurance platforms as airborne
surveillance platforms in urban environments.

The different algorithms available in the literature for tracking UAVs using
radar systems are provided in Table IX. In Table IX, the type of UAV, radar
type, tracking features, and tracking algorithm from different literature
references are summarized. From Table IX, we observe that different
derivations of Kalman filters are frequently used to estimate the future
position of the target based on the current measurements [97, 92, 98, 99, 100,
101]. The major advantage of the Kalman filter includes short convergence time
and accuracy in real-time tracking scenarios. The tracking features used
mainly by Kalman filters for tracking are position and velocity. In some
scenarios e.g., in [92], angle information was not available, however, using
different measurements from different bi-static radar pairs, the Kalman filter
was able to track the UAV. In [50], a Hough transform and linearly distributed
features of micro-Doppler are used for the detection and tracking of a UAV.

TABLE X: Different types of popular military radar systems for the detection, tracking, guidance, and classification of different types of aerial vehicles including UAVs. Name |  Type |  Installation platform |  Purpose |  Possible aerial vehicles |  Frequency |  Coverage (Az., El.) |  Range |  Antenna
---|---|---|---|---|---|---|---|---
AN/SPY6 [118] |  Search |  Ground/shipboard |  Early warning |  Aerial vehicles |  S-band |  360∘ |  Not specified |  AESA
AN/TPQ-53 [119] |  3D search & track |  Ground (immobile) |  Early warning |  Aerial vehicles |  1215-1400 MHz |  360∘,90∘ |  1000 km |  AESA
AN/TPY-2 [120] |  Surveillance, track, classify aerial vehicles |  Ground (mobile) |  Long range, very high altitude |  Aerial projectiles |  X-band |  $\pm 60^{\circ}$,90∘ |  4700 km |  Digital antenna array
AN/APY-12 [121] |  Airborne surveillance |  Airborne |  Imaging |  Ground, aerial vehicles |  X-band |  Ground imaging |  Not specified |  SAR
AN/MPQ-53 [122] |  Control radar |  Ground (mobile) |  Surveillance, IFF, tracking/guidance, ECM |  Aerial projectiles |  G/H-bands |  360∘,65∘ |  100 km |  PESA
AN/SPG-62 [123] |  Guidance |  Ground/shipboard |  Aerial vehicle illumination |  Aerial vehicles |  I/J-Bands |  Not specified |  305 km |  CW
91N6 [124] |  3D surveillance & track |  Ground (mobile) |  Early warning |  Aerial vehicles |  2.9-3.3 GHz |  360∘,90∘ |  600 km |  Phased array antenna
TPY-4 [119] |  Search, track |  Ground (mobile) |  Close range |  Rockets/artillery |  S-band |  360∘,90∘ |  60 km |  AESA
EL/M2084 [125] |  Guidance |  Ground (mobile) |  Long range |  Projectiles interception |  S-band |  360∘,80∘ |  470 km |  3D AESA
IBIS 150 [126] |  Tracking and guidance |  Ground (mobile) |  Tracking aerial vehicles |  Aerial projectiles |  S-band |  Not specified |  130 km |  PESA
TABLE XI: Popular counter-UAV radar systems [127, 128].  Product name |  Manufacturer |  Platform |  Ref.
---|---|---|---
NM1-8A drone radar system |  Accipter, Canada |  Ground-based |  [129]
Spartiath |  ALX Systems, Belgium |  UAV/Ground-based |  [130]
Gamekeeper 16U |  Aveillant, United Kingdom |  Ground-based |  [131]
Harrier Drone Surveillance Radar |  DeTect, Inc, United States/United Kingdom |  Ground-based |  [132]
RadarOne |  DroneShield, Australia |  Ground-based |  [133]
RadarZero |  DroneShield, Australia |  Ground-based |  [134]
SABRE |  DRS/Moog, United States |  Ground-based |  [135]
GroundAware |  Dynetics, United States |  Ground-based |  [136]
Red Sky 2 Drone Defender System |  IMI Systems, Israel |  Ground-based |  [137]
SharpEye |  Kelvin Hughes, United Kingdom |  Ground-based |  [138]
SR-9000S |  Meritis, Switzerland |  Ground-based |  [139]
Drone Detection Radar |  Miltronix, United Kingdom |  Ground-based |  [140]
EAGLE |  MyDefence Communication ApS, Denmark |  Ground-based |  [141]
1L121-E |  NNIIRT, Russia |  Ground-based |  [142]
3D Air Surveillance UAV Detection Radar |  OIS-AT, India |  Ground-based |  [143]
OBSIDIAN |  QinetiQ, United Kingdom |  Ground-based |  [144]
Multi-Mission Hemispheric Radars |  RADA Electronic Industries, Israel |  Ground-based |  [145]
Elvira |  Robin Radar Systems, Netherlands |  Ground-based |  [146]
Drone Sentinel |  Advanced Radar Technologies, Spain |  Ground-based |  [147]
RR Drone/radar detection system |  Aaronia, Germany |  Ground-based |  [148]
Fortem TrueVIEW radar |  Fortem Technologies |  Ground-based and airborne |  [149]
EchoFlight |  Echodyne corp. |  Airborne |  [150]
LSTAR (V)2 |  SRC Inc. |  Ground-based |  [151]
$60$ GHz FMCW MIMO |  Custom built |  Ground-based |  [152]

The popular modern military radar systems for the detection, tracking, and
classification of aerial vehicles including UAVs are provided in Table X. The
radar systems in Table X use different frequency bands for search, tracking,
guidance, and classification. It can be observed that the majority of modern
military radar systems have long-range and use phased array radar systems.
Also, the majority of such radar systems are ground-based and mobile, and have
$360^{\circ}$ azimuth coverage. Moreover, Table XI provides modern counter-UAV
radar systems. These radar systems are specifically designed to counter
different types of UAVs. The name of the radar system, installation platform,
and manufacturer information are also provided in Table XI. The majority of
the counter-UAV radar systems are ground-based and provide small to medium-
range detection of UAVs.

###  IV-C Classification of UAVs Using Modern Radar Systems

There are many methods available in the literature for the classification of
UAVs using modern radar systems [153, 154]. The main idea is to compare
multiple features of the UAVs obtained from measurements with the database of
stored features of different types of UAVs. Some of the important
classification features are size, shape, velocity, maneuverability, and
propeller motion patterns at different altitudes of a UAV flight. The features
are analyzed in both time and frequency domains. In order to accurately
classify a UAV, the following conditions should be met: 1) the scattered
signal should be independent of the operating frequency, aspect angle, and
polarization; 2) the noise, clutter, and interference from different sources
should be minimum; and 3) there should be sufficient features to classify a
UAV. Furthermore, the classification accuracy using a radar system depends on
the degree of range and angular resolution, and clutter rejection capability.

TABLE XII: Classification of UAVs by radar systems using different types of classification algorithms. UAV type |  Radar/sensor type |  Classification features |  Classification algorithm |  Ref.
---|---|---|---|---
Metafly, MavicAir2, Parrot Disco |  FMCW radar |  Micro-Doppler signatures from radar spectrogram dataset |  Deep learning |  [155]
See Table 7.1 in [156] |  CW radar, automotive radar |  Diameter of the rotor |  Support vector machine, Naive Bayes |  [156]
Mobile objects in air |  Mobile autonomous radar stations |  Image of the environment |  Neural network algorithm |  [157]
Planes, quadrocopter, helicopters, stationary rotors, and birds |  X-band CW radar |  Features based on micro-Doppler signatures of aerial vehicles |  Support vector machine, Naive Bayes |  [158]
Multi-rotor UAVs and birds |  S-band BirdRad system |  Polarimetric parameters provided in Table I of [114] |  Nearest neighbor classifier |  [114]
Rotary UAVs |  FMCW radar and acoustic sensor array |  Spectral power bins |  Neural network algorithms |  [159]
DJI Matrice 600 Pro, DJI Matrice 100, Trimble zx5, DJI Mavic Pro 1, DJI Inspire 1 Pro, and DJI Phantom 4 Pro |  Surveillance radar operating at 15 GHz and 25 GHz |  RCS of UAVs |  15 different classifiers provided in Table IV of [30] |  [30]
Multi-rotor UAVs |  X-band CW radar |  Features based on micro-Doppler signatures of the UAVs |  Spectrographic pattern analysis of UAVs |  [160]
Fixed wing and multi-rotor UAVs |  FMCW surveillance radar |  Features based on micro-Doppler signatures |  Total error rate minimization based classification |  [161]

Major challenges for the classification of UAVs using radar systems include
determining the type and intent of the UAV, discriminating micro-UAVs from
flying birds, identifying the number and type of UAVs flying in a swarm,
complexity due to a large amount of training and real-time data, and optimum
feature extraction. The popular methods for the classification of UAVs using
radar systems in the recent literature are based on RCS and micro-Doppler
signatures of the UAVs. The RCS is either taken as number values plotted
across azimuth angles or as images. The micro-Doppler signatures obtained from
spectrogram and cepstrogram plots are commonly used to classify small UAVs
[162]. The classification methods for UAVs using radar systems can be broadly
divided into AI and non-AI-based classification methods. The AI-based
classification methods are dominantly used in the recent literature compared
to non-AI-based classification methods.

AI algorithms learn the given features of a UAV using training data and use
different algorithms for UAV classification [163, 164]. Popular AI algorithms
for the classification of UAVs using radar systems include neural networks. In
[165], a radar automatic target recognition (RATR) algorithm that used CNN and
a high range resolution profile (HRRP) algorithm were presented. The proposed
method using RATR and HRRP was shown to work effectively even when the
training data was sparse. In [166], CNN was used for the classification of
UAVs and birds. The radar signatures and CNN were used for distinguishing of
UAVs from birds in [166]. Other literature using different neural network
algorithms and radar data for the classification of UAV are available in [155,
58, 59, 167, 168].

In [169] a boosting classifier was used on the radar measurements of birds and
small UAVs. The micro-Doppler radar measurements in the form of time-velocity
diagrams and a boosting classifier were used to distinguish different types of
UAVs and birds. The boosting classifier was shown to perform better compared
to the support vector machine (SVM). In [170], different AI classification
algorithms including grading boosting method (GBM) were applied to the
features obtained from range fast Fourier transform (FFT) of mmWave FMCW radar
measurements. A classification accuracy of $95.6~{}\%$ was achieved using GBM
classifier. In [171], the Naive Bayes classifier and other AI algorithms were
used for the classification of UAVs based on RCS measurements. The
classification results of different classifiers were also compared. Similarly,
SVM and logistic regression [82], and hybrid CNN-memetic algorithm [172] were
used for the classification of UAVs using radar data.

Non-AI algorithms are also used for the classification of UAVs using radar
data. In [62] a statistical-based analysis of the RCS of different types of
UAVs was carried out for classification. The RCS measurements of different
UAVs were carried out in an anechoic chamber at $15$ GHz and $25$ GHz. The
best statistical model fitting on the measurement data was carried out. The
class conditional probability density functions (PDFs) based on the best match
for a given UAV form the basis of classification. In [173] a symmetrical
feature based on the motion of the UAV propellers during the approaching and
receding phases was used to distinguish UAVs from birds. The symmetry feature
was extracted from the micro-Doppler signatures of the radar returns. A peak
extraction method based on the micro-Doppler returns provided the symmetry
feature. In [158], a CW radar operating at $9.5$ GHz was used to extract
classification features from the micro-Doppler signature of a UAV. The
eigenpairs obtained from the correlation matrix of the micro-Doppler
signatures were used as features for classification. Different types of UAVs
and birds were classified using this approach.

Another non-AI-based classification method using FMCW radar was provided in
[161], where radar returns from different UAVs and non-UAV objects were
collected. A classification method based on the pattern of micro-Doppler
returns was developed. The proposed method showed higher classification
accuracy compared to other classification methods. In [174], a two-step UAV
classification method was used. In the first step, a cadence velocity diagram
was used to obtain the micro-Doppler features and in the second step, the data
from the current and previous steps were jointly arranged for classification.
The proposed method was tested at distributed locations for the classification
of UAVs. In [50], Hough Transform was used for the detection and
classification of a multi-rotor UAV from micro-Doppler returns.

Different algorithms used for the classification of UAVs using radar systems
are provided in Table XII. It can be observed that the majority of the current
research relies on AI algorithms for the classification of UAVs. In Table XII,
deep learning, SVM, Naive Bayes, neural network, nearest neighbor, and
discriminant analysis were used for UAV classification and provided in [155,
156, 157, 158, 114, 159, 175]. Non-AI classification methods are also provided
in Table XII. In [30], non-AI classification based on statistical learning was
used in addition to machine and deep learning. It was observed that even
though statistical learning provided good classification results, the
computational time was large compared to machine and deep learning.
Furthermore, non-AI UAV classification methods in Table XII used micro-Doppler
signatures and spectrographic pattern analysis and total error rate
minimization for classification [160, 161].

In some studies, both AI and non-AI classification methods are used for
comparison. For example, in [30], different classification algorithms from
statistical learning, machine learning, and deep learning were used and a
comparative analysis of UAV classification techniques based on the RCS was
provided. The RCS of six different types of UAVs was measured at $15$ GHz and
$25$ GHz in an anechoic chamber. For example, assuming equal apriori
probabilities for each of the UAV type or class, the statistical rule for UAV
classification is given in [30] as

$\displaystyle\begin{aligned} \widehat{C}&=\arg\max_{C=1,2,\cdots,M}{\ln
P({C}=j|\bm{\sigma})},\\\ &=\arg\max_{C=1,2,\cdots,M}{\ln
P(\bm{\sigma}|C=j)},\end{aligned}$ (6)

where $P(C=j|\sigma)$ is the posterior probability for a $j^{\rm th}$ class,
and $P(\sigma|C=j)$ is the conditional class density, and there are $M$
different UAV classes.

##  V DCT-U using Communication Systems

In this section, we highlight the similarities between radar and communication
systems, and the advantages of communication systems for DCT-U. Furthermore,
JC&S in active mode and the use of communication systems in passive mode for
DCT-U are provided. The limitations of communication systems for the DCT-U are
also discussed.

###  V-A Similarities and Differences of Communication and Radar Systems

There are many similarities between communication and radar systems.
Therefore, communication systems can be used for DCT-U. The similarities and
differences between the communication systems and radar systems are summarized
as follows:

  * •

Both communication systems and radar systems use radio waves at different
frequencies.

  * •

The radio wave propagation through free space takes place similarly for
communication systems and radar systems.

  * •

Both radar systems and communication systems use a signal generation stage and
an RF stage. The signal generation stage typically involves waveform
generation, shaping, coding and modulation, and encryption. The RF stage
includes amplification and beam steering (by changing the signal phase across
antenna elements) from the antenna array.

  * •

The RX of a communication system and a radar system generally includes a low
noise amplifier, matched filter, decoder, decrypter, demodulator, and
detector.

  * •

Both communication systems and radar systems are time-critical systems.

  * •

Majority of communication systems and radar systems use encryption and
authentication.

  * •

The major difference between the communication systems and the radar systems
is the transmit power. The pulse-based radar systems transmit radio waves at
much higher power compared to common RF communications. The high transmit
power is obtained by using amplifiers and high-gain antennas.

  * •

Complex frequency hopping patterns are frequently used in radar systems
compared to communication systems to avoid jamming.

  * •

The selection of frequencies for the communication systems takes into account
the transmission (or penetration losses) through the building materials.
However, for radar systems, the transmission through materials is not mainly
considered during frequency selection and the main focus is on the reflection
properties.

  * •

Complex encoding and signal processing techniques are used for communication
systems. The techniques help to attain a high data rate and serve multiple
users simultaneously. On the other hand, for radar systems, simple pulse
signals are commonly used because the main focus is reliability.

  * •

The radar systems operate mainly in the line-of-sight (LOS), whereas, the
communication systems can operate in the LOS and non-LOS (NLOS).

  * •

The RX sensitivity is higher for radar systems compared to common
communication systems.

  * •

The TX and RX are generally co-located in a radar system (monostatic radar),
whereas TX and RX are apart in the communication systems.

###  V-B Advantages of Communication Systems for DCT-U

The major advantages of communications systems for DCT-U can be listed as
follows:

  * •

Communication systems offer a thorough coverage in the majority of urban areas
compared to radar systems.

  * •

Communication systems can operate and subsequently detect and track UAVs in
NLOS conditions. On the other hand, the majority of radar systems require a
LOS path with the UAV for precise detection and tracking.

  * •

Multiple types of communication systems are available in an urban area, e.g.,
FM and amplitude modulation radio broadcasts, television broadcasts, and
mobile and satellite communications. Each communication system uses a
particular frequency band and communication methodology. Moreover, different
types of communication systems offer long, medium, and short-range
connectivity. Overall, the diverse types of communication systems available in
an urban area can help DCT-U in different scenarios.

  * •

The presence of multiple communication nodes along the path of a non-
cooperative UAV can help to update the localization estimates.

  * •

The height of the antenna(s) on the communication towers is generally selected
to maximize coverage in a given area. The height of the majority of the
communication tower antennas is such that small UAVs flying at typical heights
(e.g., lower than $400$ feet in the United States under FAA Part $107$ Rules)
can be detected and tracked through a LOS path.

  * •

Communication systems can be used to obtain the Doppler signature of the UAV,
which can help to classify a UAV.

  * •

The communications using satellites offer ubiquitous and LOS coverage on the
ground mainly due to the height of the satellites above the ground. The
ubiquitous LOS coverage by satellites can help in the detection and tracking
of UAVs.

  * •

Modern communication systems, e.g., $5$G and beyond use phased arrays and can
electronically steer antenna beams similar to phased array radar systems.

  * •

Different communication systems update the channel state information (CSI) at
regular intervals of time, e.g., massive MIMO systems. These periodic CSI
updates can help in UAV detection.

  * •

Both active and passive DCT-U can be achieved using communication systems.

  * •

Popular communication waveforms, e.g., orthogonal frequency-division
multiplexing (OFDM) can be used for JC&S. Moreover, both uplink and downlink
channels can be used for sensing in a JC&S system using a mobile network.

  * •

A significant research literature and standardization details are available
for communication systems in the public domain that can help research for UAV-
based detection, tracking, and classification.

###  V-C DCT-U Using JC&S

JC&S also known as joint communication and radar systems has gained
significant importance in the recent decade [176]. The term sensing generally
includes DCT. The majority of JC&S literature uses active radar and mobile
communication networks. The major goal of JC&S is to integrate communications
and sensing of the environment through a single device and preferably using a
single transmitted signal. The JC&S can offer improved spectral efficiency,
and reduce cost, size, and power consumption. Advanced hardware and software
including complex signal processing techniques are required to make the
integration of the two domains efficient. JC&S can also be used for DCT-U.
There are studies available in the literature that provide JC&S techniques for
DCT-U [177, 178].

In [16], a survey on JC&S in mobile networks was provided. In the survey,
studies on coexisting radar and communication systems and their limitation
were discussed. Mainly three types of JC&S systems were covered namely, radar
centric and communication centric designs, and joint design and optimization.
Furthermore, modifications were discussed to integrate sensing operations into
the current communication-only architecture. UAV detection was provided as an
application of the JC&S systems. In [179], JC&S using MIMO systems was
discussed. Three novel JC&S models using MIMO and cloud random access
networks, UAVs, and reconfigurable intelligent surfaces were discussed. The
survey provided different Internet of things (IoT) scenarios for JC&S.
Different waveform designs for MIMO-based JC&S were also provided. Other
survey studies on JC&S are available in [180, 181, 182, 183]. We focus on
active JC&S systems in this survey.

TABLE XIII: Comparison of JC&S systems based on resources and waveform.  System |  Resources |  Waveform |  Pros |  Cons |  Ref.
---|---|---|---|---|---
JC&S with different transmit signals |  Resources are generally shared |  Different types of waveform |  1) The design of waveform for communication and sensing is approximately independent, and 2) small mutual interference |  1) Complex and expensive hardware, and 2) low spectral efficiency |  [184, 185, 186, 187, 90]
JC&S with same transmit signals |  Resources can be shared or dedicated |  Same waveform |  1) Shared TX and RX, and 2) high spectral efficiency |  Requires clock synchronization between TX and RX to avoid sensing ambiguity |  [188, 189, 190, 177]
Distributed JC&S |  Resources are shared |  Different waveform optimized for sensing and communications, respectively |  1) Improved spectral efficiency, and 2) less complex TX and RX compared to different waveform JC&S system |  1) Coordination among the sensing and communication nodes require synchronization that is challenging if far apart, and 2) interference between communication and sensing nodes |  [191, 192]
Cognitive radio |  Resource shared cognitively |  Different waveform |  Efficient spectral usage |  1) Interference can affect the primary users, and 2) performance of secondary users depends on the cognitive algorithm and surroundings |  [193, 194]

####  V-C1 Active JC&S Using Different Signals for Communications and Sensing

In this type of JC&S, the communication and sensing signals are separated in
frequency, time, code, and polarization. The communication and sensing
hardware is often partially shared. In [184] a JC&S system comprising of MIMO
radar and a configuration of sparse antenna array was used. Independent
transmit waveforms were associated with antennas resulting in different pulse
repetition periods. Moreover, by antenna selection and the ordering of
antenna-waveform pairs a high communication data rate was achieved. The sensed
target was an aerial vehicle. In [185], an intelligent transportation system
operating at mmWave was used for simultaneous communication and radar
functions. Different waveforms were used for communications and radar
functions controlled by a voltage-controlled oscillator switch.

In [186] a software-defined automotive system providing JC&S was provided. In
the proposed approach, FMCW was used to obtain the range of the target,
whereas pseudorandom (PN) code pulse was used for inter-vehicle communication.
The switching between FMCW and PN was controlled through software. In [90], a
phased array radar with six simultaneous beams was used for DCT-U. Two beams
were used for communications and the other four beams were used for radar
operation. The radar and communication beams used independent waveforms. The
system resources were cognitively scheduled among the beams dependent on the
scenario through reinforcement learning.

####  V-C2 Active JC&S Using Same Signals for Communications and Sensing

A major goal of JC&S is to design a common signal that optimizes
communications and sensing. There are many research efforts where a common
signal is used and optimized for communications and sensing. In [188],
multiple phased array beams originating from a MIMO base station (BS) were
steered in different directions to serve mobile users on the ground and detect
UAVs in space. OFDM signals with a standard cyclic prefix were used for both
communications and sensing. The variations in the channel impulse response
(CIR) were used to detect the presence of a target. The detection of a target
was considered a binary hypothesis set. The example target sensed was a UAV.
In [189], UAV-based machine type communications are considered for JC&S. Code-
division OFDM JC&S system was used through unified spectrum and transceivers.
A novel code-division OFDM signal was used for both communications and
sensing. The successive interference cancellations were used to provide code-
division gain. The code division gain was used to reduce the multiple access
interference. In [190], JC&S using orthogonal time frequency space (OTFS)
waveform was used for industrial IoT including ground vehicles and UAVs. The
problem of lack of sensing by OFTS was overcome using different pre-processing
methods to remove the effect of communication symbols in the time and
frequency domains.

(a)

(b)

(c)

Figure 11: (a) A JC&S system example in which time-frequency resources are
equally distributed between the communication and sensing operations (C
represents communications and S represents sensing). The signals for
communications and sensing operation can be the same or different; (b) a JC&S
system example with simultaneous downlink communications and active sensing.
The signals for communications and sensing are preferred to be the same in
this scenario; (c) passive sensing of a target scenario using a communication
system.

####  V-C3 Active Distributed JC&S Systems

In a distributed JC&S system the communication and sensing subsystems may not
be co-located. In a distributed JC&S system the signals are different but the
resources are shared. Moreover, the communication and sensing subsystems in a
distributed JC&S can be cooperative or non-cooperative. The cooperative
architecture requires information sharing and is complex compared to the non-
cooperative architecture. Fig. 11(a) shows a cooperative resource-sharing
example for JC&S where the communication and sensing subsystems are co-
located. In [191] a joint MIMO matrix completion radar and communication
system was proposed that operated concurrently by sharing the spectrum. The
sharing of spectrum helped to reduce the interference between the two systems.
Both cooperative and non-cooperative approaches were used. The target
considered for JC&S was an aerial vehicle. In [192], coexistence and spectrum
sharing between a MIMO radar and a downlink multiuser-MIMO communication
system was provided. The transmit beamforming of the MIMO downlink
communications was designed to optimize the communications and probability of
detection of MIMO radar. The beamformer proposed was robust to imperfect
channel state estimates.

####  V-C4 Active JC&S Using Cognitive Radios

In [193], a rotating radar was used as a primary device and a communication
system as a secondary device that cognitively accessed the available spectrum.
The secondary communication device was allowed to transmit as long as the
interference levels were bounded within the range allowed by the primary radar
device. The secondary communication device was orthogonal frequency-division
multiple access (OFDMA) based operating in non-contiguous cells that used a
given dedicated spectrum and a shared spectrum with the radar. Similarly, in
[194] the effect of opportunistic access to the radar spectrum by a
communication link was investigated. The spatial and temporal opportunities
were explored for the co-existence of radar and communication system keeping
the interference level low to the primary radar device while maintaining the
communication throughput. Table XIII provides a comparison of JC&S systems
based on waveform and resources.

####  V-C5 Active Radar Centric JC&S Design

In a radar-centric JC&S design, the communications are realized in the primary
radar system. The radar systems have generally long ranges and communication
systems can take advantage of the long-range, however, the throughput is
limited due to the radar-specific waveform. In a radar-centric system,
multiple access channels for JC&S were considered in [195]. Spectrum-time-
space convergence was considered for the co-existence of radar and
communication systems. In [196] a MIMO radar with quasi-orthogonal multi-
carrier linear frequency modulation continuous phase modulation waveform was
used for sensing and multi-user communications. In [197] a radar was
implemented using SDR that is able to support communications. The system used
wideband communications. The wideband communications supported radar operation
and allowed communication of range-Doppler maps with the BS. There are other
related studies also [198, 199, 200] where different types of waveform were
used for radar that support communications.

####  V-C6 Active Communication Centric JC&S Design

In a communication-centric design, sensing capability is introduced to a
primary communication system. Communication systems that support sensing can
be further categorized into small-scale point-to-point communication systems,
e.g., vehicular communications or large-scale mobile networks. In [201] radar-
based sensing was implemented for the mmWave 802.11ad vehicular communication
system. A full duplex radar operation was considered due to limited self-
interference and sufficient isolation. In [202] velocity and range estimation
of multiple scatterers in an OFDM vehicular communication system was carried
out by different algorithms. JC&S in $5$G mobile networks using compressed
sensing was introduced in [203]. The compressed sensing helped in the target
detection using $5$G communication signals.

The sensing in a communication-centric mobile network can take place at the
downlink or uplink. Fig. 11(b) shows a downlink JC&S scenario using a mobile
network. The downlink JC&S can be active or passive, whereas, the uplink JC&S
is generally passive. A passive sensing scenario using a mobile network is
shown in Fig. 11(c). In an active downlink JC&S, the TX and RX are generally
co-located. As the TX and RX are co-located, the synchronization between the
TX and RX can be easily achieved. Also, the active downlink JC&S requires
full-duplex capability. In [204], the JC&S system using MIMO was used. A
single TX communicated with the downlink mobile user and simultaneously
detected targets in the surroundings. Different design criteria were
introduced to mitigate the multiuser interference including omnidirectional
and directional antennas, and optimized waveforms. In [205], both uplink and
downlink sensing using a mobile network was demonstrated and the same signal
was used for communications and sensing.

####  V-C7 Active Non-centric JS&S Design

There is a third category where the design of JC&S is not centric around
existing radar or communication systems. These offer better freedom and a
balanced approach in the design of waveforms, resource distributions, and
corresponding algorithms for communications and sensing. In [182], waveform
designs and signal processing aspects for JC&S at mmWave were covered without
a centric design. Both UAV and ground vehicle targets were considered for
JC&S. In [206], ultra-massive JC&S was provided at the terahertz band. In the
proposed JC&S system both model-based and model-free hybrid beamforming was
used. The BS consisted of ultra-massive MIMO and multiple beams were generated
toward the multi-antenna mobile users and radar targets. Another non-centric
JC&S system was proposed in [207]. The proposed system used analog antenna
arrays and two sub-beams were superimposed to generate an optimal combined
beam. The optimal beam was used for JC&S.

###  V-D DCT-U Using Communication Systems in Passive Mode

Communication signals reflected from UAVs can be passively monitored for
DCT-U. Commonly used communication systems for DCT-U are GSM, LTE, $5$G, WiFi,
digital audio and video broadcasting, and satellites. Table XIV lists some
examples where different communication systems are used for DCT-U in passive
mode with further details as follows.

TABLE XIV: Example passive radar systems that passively capture communication signals reflected from UAVs for DCT-U. Communication System |  Specifications |  Bistatic or multistatic radar |  Ref.
---|---|---|---
GSM |  A passive radar with multibeam and multiband capability is used for the detection of low RCS UAVs using GSM signals. Field experiments were carried out in a remote area. Time-Doppler map is used for detection of UAV |  bistatic |  [91]
LTE |  A SDR-based LTE passive radar is used for the detection of a UAV in an urban environment. A frequency division duplex downlink LTE signal is used as an illuminator of opportunity |  Bistatic |  [208]
$5$G |  A passive radar with Rényi entropy using $5$G network is used for the detection of UAVs. The Rényi entropy helps to select time frames at the downlink channel in both frequency and time |  bistatic |  [209]
WiFi |  A WiFi-based passive radar is used for the detection of UAVs. In addition to detection, $3$D localization of the UAV can be obtained using a quad-channel RX |  Multistatic |  [210]
DAB |  DAB signals are used as illuminators of opportunity for a passive multistatic radar for the detection of low-flying UAVs |  Multistatic |  [211]
DVB-T |  A DVB-T-based passive radar is used for the detection and tracking of UAVs. The range-Doppler matrix is used for the identification of UAVs |  Bistatic |  [212]
Satellite |  Passive FSR and DVB-S signals are used for UAV detection. The proposed system is similar to an air traffic monitoring system |  Bistatic |  [213]

####  V-D1 GSM

Major advantages of using a GSM network for the detection and tracking of UAVs
include the collection of detection measurements from different aspect angles
using BSs, and combining measurements from different BSs to overcome weak
measurements from a single base station. In [214], a GSM-based radar in the
passive mode was used to collect weak reflections from a UAV. Experiments were
conducted to detect and track a quadcopter using passive GSM-based radar
achieving a high detection rate. A track-before-detect method was used to
detect and track small UAVs using GSM signals. A GSM-based detection system
for small UAVs operating in the passive mode was introduced in [215]. Passive
bistatic radar measurements were carried out using GSM signals in [91] for the
detection of small RCS UAVs. Experiments were carried out in [216] using
multiple mobile telecommunication illuminators including GSM in the passive
mode for the detection of aerial targets.

####  V-D2 LTE

Major benefits of LTE include variable bandwidth available from $1.4$ MHz to
$20$ MHz and high range and velocity resolution compared to GSM. Also, the
ambiguity function in LTE results in lower sidelobes, and the global coverage
allows coordinated passive sensing. Similar to GSM, an LTE network can be used
for the detection and tracking of UAVs in the passive mode. A USRP-based setup
in [217] used LTE signals from multiple TXs and a single RX was used for the
detection of UAV in the passive mode. In [218], a synthetic aperture radar
(SAR) image of the UAV was obtained using OFDM signals of an LTE system. A
passive LTE-based radar system was used in [208] for the detection of UAVs.
Experiments were conducted to demonstrate the capability of the passive LTE
radar to detect low-flying UAVs. In [219], an LTE-based multistatic radar was
used for the detection of UAVs. A new target detection-based feature was added
to the existing downlink LTE signal.

####  V-D3 $5$G and Beyond

To fulfill high data rate requirements, mmWave, and higher frequency bands are
envisioned for $5$G and beyond communications. Salient features of $5$G
communications include dense network coverage through a large number of access
points and connected devices, highly directional antenna beams, and beam
steering capabilities. In [220], a passive $5$G radar was used for the
detection of UAVs. The system took advantage of variable resource allocation
in a $5$G system for the detection of low RCS UAVs. In [221], a fully
cooperative $5$G cellular system was used as a source of illumination for a
passive radar for target detection. A moving target was illuminated by the
$5$G BS in a bistatic geometry. In [209], Rényi entropy was used for the
selection of time frames in a downlink channel of $5$G system. The resource
allocation was found to have a significant effect on the detection and range
resolution of a target.

####  V-D4 WiFi

The WiFi signals reflected from targets in the surroundings can be captured
passively and subsequently used for detection. In [210], WiFi transmissions
were used for the detection and $3$D localization of UAVs and small planes
using a passive radar. A passive bistatic radar based on WiFi transmissions is
also provided in [222, 223]. In [223], a WiFi-based passive bistatic radar
using least square adaptive filtering and moving target indicator (MTI) was
used. The proposed approach provided a cost-effective solution that can be
implemented using SDR for the detection of low-flying aerial vehicles.

####  V-D5 Digital Audio Broadcast

Digital Audio Broadcast (DAB) has been a popular source of communication to
large populations. DAB can also be used for the detection of UAVs in the
passive mode. A passive radar in the VHF band and using DAB transmissions were
used for the detection of a fixed-wing UAV in [224]. The detection range of
the fixed-wing UAV reported was $1.2$ km. In [225] FM (using analog signals)
radio-based passive radar sensor was used. The sensor can be used for air
surveillance. Similarly, passive multi-static aerial surveillance using DAB
signals was carried out in [226]. The Doppler and bi-static time difference of
arrival measurements were used and a multi-hypothesis tracking algorithm was
developed to help at different stages of tracking.

In [211], DAB-based passive multistatic radar was used to detect stealth UAVs.
Moreover, a passive multistatic radar based on the FM radio signal
transmissions in [227] was used to detect aerial vehicles. The tracking was
carried out using three passive radars placed at different locations. In
[228], a passive coherent location (PCL) system using DAB, FM, and cellular
communication signals was used. The PCL was used to detect UAVs.

####  V-D6 Digital Video Broadcast Terrestrial

Similar to DAB, digital video broadcast terrestrial (DVB-T) signals can be
used for the detection and tracking of UAVs. The potential of a passive radar
using DVB-T was shown in [212]. Experimental data for the detection and
identification of multi-rotor and fixed-wing UAVs was provided. It was claimed
that passive radars can play an important role in the detection of different
types of UAVs at long ranges using DVB-T transmissions. The potential uses of
DVB passive radar for the detection and identification of different small UAVs
were provided in [212]. The detection and identification using DVB passive
radar helped to counter illegal UAV flights. The detection of UAVs using
DVB-T2 passive coherent radar was provided in [229].

A passive radar using DVB-T illuminators of opportunity was used for $4$D
(range, Doppler, azimuth, and elevation) detection and tracking of small UAVs
in [230]. The approach used a uniform linear array and two separate antennas
for the estimation of range, Doppler, azimuth, and elevation angles of the
UAVs. A passive multistatic digital TV-based radar for UAV detection was
provided in [231]. Passive digital TV-based radar was used for the detection
and tracking of UAVs in a highly cluttered scenario near airports in [231]. In
[232] it was observed that the strong reflections from large RCS of commercial
planes made software-based detection and tracking of small RCS UAVs difficult.
Long coherent processing intervals and multi-stage CLEAN algorithms were,
therefore, used to filter strong reflections from commercial planes.

####  V-D7 Satellites

Satellite communications can be used for the detection and tracking of UAVs
near the ground. However, the long-distance of the satellites from the ground,
low data rates, and small RCS of common UAVs make detection and tracking
challenging. In [213], a DVB satellite (DVB-S) was used for the detection of
UAVs. The micro-Doppler signature from rotating blades was used for UAV
identification. To overcome the challenges of UAV detection by satellite
networks, forward scattering radar (FSR) configuration was used in the passive
mode. The passive DVB-S and FSR were also used in [233] for the detection of
multi-rotor UAVs and performing micro-Doppler analysis of the copter UAVs. In
[234], DVB-S was used for the detection of multi-rotor UAVs and micro-Doppler
signature extraction. The micro-Doppler signature helped in the UAV
classification. A reference channel and a surveillance channel were used. The
reference channel pointed to the satellite for reference signaling, whereas
the surveillance channel pointed to the UAV. The two channels were used to
calculate the cross-ambiguity function which assisted in UAV detection.

###  V-E Limitations of Communication Systems for DCT-U

There are many limitations of communication systems when used for the DCT-U
(in active or passive modes). Further research efforts are required to address
these limitations. The limitations are summarized as follows:

  * •

A common signal that optimizes both communications and sensing in JC&S is
desired. However, designing a signal that optimizes communications and sensing
simultaneously is challenging.

  * •

In JC&S systems where different signals are employed for communication and
sensing, sharing the same resources can lead to interference between the
communication and sensing signals.

  * •

In a JC&S system that utilizes a common signal for both communications and
sensing, a necessity for full duplex operation arises.

  * •

Synchronization between the TX and the RX is a major challenge for JC&S
systems when TX and RX are not co-located.

  * •

The effectiveness of sensing using passive radar when the target illuminator
is a communication system depends on the characteristics of the communication
system.

  * •

The accuracy of a passive radar based on the communication system is dependent
on many factors including communication signal type, interference levels, and
propagation environment. The accuracy is generally low compared to active
radar systems.

  * •

The range and coverage of target detection in passive mode rely on the
strength of the communication signals. Communication systems operating in
high-frequency bands, such as $5$G, may exhibit limited range for passive mode
target detection.

  * •

The communication system-based passive radar systems are complex and often
require multiple RX nodes and significant training data for correct detection.

  * •

The tracking of a UAV requires multiple communication nodes that coordinate
with each other. The coordination for tracking UAVs requires additional
resources and infrastructure.

  * •

The classification of UAVs requires training data. The training data obtained
through a given communication system may not be applicable directly to another
type of communication system.

##  VI DCT-U using Passive monitoring of RF SOO Radiated from UAVs

In this section, the passive monitoring of RF signals radiated from UAVs for
DCT-U is discussed. Note that this is different from what is covered in
Section V-D, since here we consider signals explicitly transmitted by the
UAVs. The limitations of DCT using passive monitoring of SOO radiated from
UAVs are also provided.

Figure 12: A scenario of passive capturing of RF SOO between UAV and GS using
RF sensor.

###  VI-A RF Signals Radiated from UAV for DCT-U

The RF signals radiated from UAVs for remote control and data transfer can be
used for DCT-U. The RF signals are captured either between two communicating
UAVs or between a UAV and a ground station (GS). A scenario of passive
capturing of RF SOO between the UAV and GS for DCT-U is shown in Fig. 12.
Different methods are available in the literature for capturing RF signals
radiated from UAVs and distinguishing these RF signals from other
communication signals for the detection and tracking of UAVs. Furthermore, the
classification of UAVs is performed based on the features of the detected RF
signals. In [235], a passive radio surveillance system was used to capture and
analyze RF fingerprints of the radiated signals between UAV and GS in the
presence of WiFi and Bluetooth interference. After the signal was confirmed to
be from a UAV, the UAV was classified using different machine learning
methods.

TABLE XV: Unique aspects of DCT-U that are based on the passive detection of RF signals emitted by UAVs are summarized. Ref. |  RF SOO considered |  # of UAVs/ controllers |  AI/ML techniques used |  Hardware for RF signal detection |  Unique aspects
---|---|---|---|---|---
[235] |  $2.4$ GHz center frequency, $5$ M samples |  $17$ UAV controllers |  kNN, discriminant analysis, SVM, neural network, RandF |  High-frequency oscilloscope, directional antenna |  Detection and classification of UAVs was performed using RF energy detection between UAVs and ground controller in the presence of WiFi and Bluetooth interferences and using machine learning algorithms
[236] |  $2.4375$ GHz center frequency, $60$ MBits/s sampling rate, $28$ MHz bandwidth |  $7$ UAVs |  kNN, logistic regression, CNN |  SDR and directional antenna |  RF signals between UAV and ground controller were collected in the presence of WiFi and Bluetooth interferences and CNN algorithm was used for UAV classification
[237] |  $2.4$ GHz center frequency, $20$ Mbit/s sampling rate |  $2$ UAVs |  SVM, random forest, kNN, Naive Bayes, ensemble learning |  X$310$ USRP, UBX-160 daughter board, and circular antenna array |  Detection and tracking of UAVs was carried out based on multidimensional signal features and machine learning algorithms
[238] |  $2.400$ GHz$-2.483$ GHz frequency range, $20$ MHz bandwidth |  $1$ UAV |  $-$ |  RF sensor and broadband omnidirectional antenna |  Multiple RF sensors placed at different locations were used to localize and track a UAV using extended Kalman filter
[239] |  $2.437$ GHz center frequency, $100$ kHz sampling rate |  $1$ UAV |  $-$ |  SDR and directional antenna |  The frequency spectrum of signals between UAV and GS were analyzed and observed distinctive amplitude peaks at certain frequencies for detection of UAVs
[240] |  $2.4$ GHz center frequency |  $2$ UAVs |  Random tree, random forest, Logit boost |  Guardian WiFi sniffer, and laptop |  The wireless traffic generated by different UAVs was captured using a WiFi sensor, and statistical fingerprinting analysis was applied to the WiFi traffic for UAV detection
[241] |  $2.4$ GHz center frequency |  $2$ UAVs |  kNN, SVM, RandF |  SDR, directional and omnidirectional antennas |  UAV detection was carried based on fingerprinting of wireless signals emitted by the UAV by a wireless sensor. The extracted features from the RF signal were used for the classification of UAVs
[242] |  $2.4$ GHz center frequency |  $3$ UAVs |  Deep neural network |  USRP-$2943$ |  A brief database of RF signals emitted from UAVs are collected and used for classifying UAVs using deep neural network
[243] |  $2.422$ GHz center frequency |  $3$ UAVs |  Convolutional neural network |  USRP-$2943$R |  UAV detection and identification is carried out using RF communications between UAV and GS and convolutional neural network
[244] |  $2.4$ GHz $-$ $2.4835$ GHz center frequency |  $3$ UAVs |  Convolution neural network |  USRP- $2943$ |  UAV detection and classification is performed with the help of UAV RF signal database and convolutional neural network
[245] |  $2.3$ GHz$–$$2.5$ GHz center frequency, $9.8$ GHz bandwidth |  $1$ UAV |  Artificial neural network |  Passive receiver and computer |  Clutter suppression and artificial neural network is used for the RF-based detection of micro-UAVs
[246] |  $900$ MHz center frequency |  $2$ UAV TXs and $5$ UAV RXs |  Deep convolutional neural network |  USRP B$200$mini |  Customized RF fingerprinting and deep learning is used for UAV identification
[247] |  $5.725-5.825$ GHz, $2.401-2.481$ GHz frequency range |  $10$ UAVs |  Single and multi-neural network |  Spectrum analyzer, and USRP X$310$ with UBX $160$ daughterboard |  Identification of UAVs in a swarm was carried out by learning subtle changes in the transmitted waveform and using NN
[248] |  $2.4$ GHz center frequency |  $10$ UAV controllers |  kNN, SVM, SqueezeNet |  Digital oscilloscope and directional antenna |  Wavelet transform analytics were used for feature extraction and RF fingerprints of the signals emitted from UAV flight controller were used for identification and classification of UAVs
[249] |  $2.4$ GHz center frequency |  $8$ UAVs |  $-$ |  $8260$ WiFi adapter on a personal computer |  Detection and mode identification based on encrypted WiFi traffic was carried out for UAVs using machine learning

The effect of interference from WiFi and Bluetooth signals on the detection
and classification of UAVs through the RF analysis of captured signals was
provided in [236]. It was shown that by using CNN and machine learning
classification, a high percentage of accuracy for the detection and
classification of different types of UAVs was achieved. There are also similar
studies where RF fingerprints of the radiated signal between UAV and GS were
captured and analyzed passively to detect UAVs [175, 240, 155].

In [237], the detection and localization of UAVs relied on the utilization of
multidimensional features extracted from the signals between the UAV and the
GS. Features included the frequency transform of the signal, power spectral
entropy, and wavelet energy entropy. Machine learning algorithms were employed
to detect UAVs based on these selected features. [242] involved the creation
of a database comprising RF signals emitted by various UAVs in diverse flight
scenarios, including being on the ground and connected to the GS, flying,
hovering, and during video recording. Using the collected data and deep neural
networks, UAVs were detected and classified.

The detection and localization of a UAV was carried out by passively
monitoring the RF signals emitted from the UAV in an open area using RF
sensors in [238]. An envelope detection algorithm and RF database of different
UAVs were compared with the measurement data for UAV detection. The time
difference of arrival was used to localize the UAV. There are other studies
available that detect UAVs based on RF signals radiated from UAVs and use
different machine learning methods [243, 244, 245, 250]. In Table XV RF SOO
radiated from UAV/controller, number of UAVs/controllers used for
classification, AI/machine learning techniques used, hardware used and unique
aspects of DCT-U based on passively capturing RF signals radiated from UAVs
are summarized.

Observations from Table XV highlight that most RF SOO-based passive UAV
detections operate at a center frequency of $2.4$ GHz. Additionally, the
hardware used in the majority of studies listed in Table XV is simple and off-
the-shelf. While deep learning offers highly accurate UAV classification, its
accuracy might diminish in scenarios divergent from the training data. Table
XVI presents a comparative performance analysis of various machine learning
algorithms employed for detecting and classifying different UAVs/controllers
using RF fingerprinting techniques in [235]. From Table XVI, it can be
observed that RandF offers the highest accuracy for the classification of
UAVs.

# of controllers |  Classifier |  Accuracy using all features $(\%)^{2}$ |  Computational time using all features $(\rm{s})^{2}$
---|---|---|---
15 |  kNN |  $97.30$ |  $24.85$
|  DA |  $96.30$ |  $19.42$
|  SVM |  $96.47$ |  $119.22$
|  NN |  $96.73$ |  $38.73$
|  RandF |  $98.53$ |  $21.37$
17 |  kNN |  $95.62$ |  $26.16$
|  DA |  $92.77$ |  $19.36$
|  SVM |  $93.82$ |  $139.94$
|  NN |  $92.88$ |  $46.04$
|  RandF |  $9.32$ |  $24.71$

TABLE XVI: Performance comparison of different machine learning algorithms for
detecting and classifying different types of UAV controllers using RF
fingerprinting (regenerated from [235])

.

###  VI-B Communication Systems for Capturing RF Signals Radiated from UAVs
for DCT-U

The communication protocols used by UAVs commonly use the same frequency bands
as used by WiFi, e.g., $2.400–2.483$ GHz and $5.725–5.825$ GHz. Therefore, RF
signals emitted by UAVs can be captured using WiFi and other communicaton RXs
operating in the same frequency band. In [240], statistical fingerprint
analysis of the WiFi signals was carried out to detect the presence of
unauthorized UAVs. The characteristics of the UAV control signals and first-
person view transmissions, features of WiFi traffic generated by UAVs, and
machine learning algorithms helped to identify a UAV in a WiFi coverage area.
Similarly, in [251], a WiFi-based statistical fingerprinting method was used
for the detection and identification of UAVs. The proposed method claimed to
achieve an identification rate of $96$%.

In [241], the detection and identification of UAVs were carried out using
captured WiFi signals from UAVs and RF fingerprinting. Firstly, a UAV was
detected based on the captured RF signals by the WiFi RX, then machine
learning algorithms were used to identify the category of the UAV. The
identification accuracy was higher outdoors compared to indoors. In [249],
machine learning algorithms were used to analyze the WiFi signals originating
from UAVs for control and video streaming. The analysis helped to detect and
identify UAVs. The proposed approach worked on the encrypted WiFi traffic also
by using the packet size and WiFi traffic inter-arrival time features. In
[252], long-range bluetooth was used to identify and track UAVs. The project
provided a beacon capability for UAVs that helped in their identification when
in the range of the bluetooth RX.

###  VI-C Limitations of Passively Capturing RF SOO Radiated from UAVs

There are limitations to passively detecting SOO radiated from UAVs for DCT-U.
These limitations include, 1) the range of the RF analysis methods using
different sensors is dependent on the strength of the radio link, and
propagation channel between the UAV and the controller. Therefore, a sensor
needs to be in the vicinity of the UAV and ground controller and the
propagation channel should be favorable, 2) if a UAV is flying autonomously
without any active radio link, or UAV is mechanically controlled, then the RF
analysis method may not be effective, 3) RF analysis method operates in a
passive mode and the precise location of the UAV cannot be precisely
determined using a single antenna, 4) UAVs equipped with ECM can be used to
deceive, hack, and jam RF analysis methods.

##  VII Future Directions

In this section, future directions for DCT-U based on the lessons learned are
provided.

###  VII-A Communication Systems

Exploration of new methods for passive UAV detection through communication
systems is a promising research area. Moreover, there is significant potential
for the future of JC&S with the utilization of $5$G and beyond mobile networks
[253, 203, 179]. The progress in JC&S can direct benefit DCT-U. Beyond the
JC&S scenarios previously discussed, opportunities exist in areas such as
microwave communication links between BSs, marine radio communications, public
safety communications, and sensor fusion from various communication systems,
all of which can be harnessed for DCT-U.

Figure 13: A laser mesh created in the air using two airborne UAVs for
detection, tracking, and classification of aerial vehicles (regenerated from
[254]).

###  VII-B Single Long-range and High-resolution Radar

Contrary to distributed sensors, a long-range, wide area coverage, and high-
resolution radar may be used. Generally, long-range radars cover a large area
and operate at low frequencies but have limited resolution. However, if a
single long-range, wide-coverage, high-resolution radar can be realized it can
provide a single point of DCT-U. Compared to distributed sensors, a single
long-range high-resolution radar can have a small processing delay. The long-
range high-resolution radar can be placed at an optimum location on the
ground, sea, or in space.

###  VII-C Quantum Radars

Quantum radars are considered to be radars of the future and can be used for
the detection and tracking of UAVs. There is limited literature available on
quantum radars currently. A study on quantum radars was carried out in [255].
The developments and design considerations were covered in this study. A major
benefit of the quantum radar concluded in the study was their extra degrees of
correlation to obtain information compared to conventional radars. The extra
degree of correlation helped to achieve high resolution and SNR.

###  VII-D Hybrid Radars

Hybrid radar systems that consist of two or more radar types can take
advantage of the strengths of different radar systems. Hybrid radars can be
useful for the detection and tracking of UAVs in a swarm. In [256], a hybrid
active and passive radar system was used. Both the active and passive radars
operated at the same frequency band for spectral efficiency. The timing
uncertainty and mutual interference due to the simultaneous operation of
radars were mitigated through min-max and weighted sum criteria. In [257] a
linear FMCW and interferometry mode was used for indoor detection and
tracking. The two modes were used for different purposes. The ranging was
carried out using FMCW radar mode and the positioning of a target was
performed using interferometric radar.

###  VII-E Sparse Signal Processing

A major limitation in the detection of UAVs at long distances using RF-based
systems is sparse signal returns. The sparse signals can be processed using
modern sparse signal processing techniques available in the literature for
better detection of UAVs [55]. For example, orthogonal codes can be used for
complete reconstruction based on sparse signal returns.

###  VII-F Open-Ended, Modular, and Reconfigurable Design

Open-ended, modular, software-defined architecture should be used for future
RF-based systems for the detection of UAVs. In an open-ended, modular, and
software-defined architecture, updates and improvements can be made without
significant changes to the overall hardware and software. Also, autonomous
runtime changes can be made easily, e.g., using AI.

###  VII-G Resilience to ECM

RF-based systems should be able to withstand modern ECM for accurate detection
of UAVs. In particular, the RF-based systems should be able to resist jamming,
hacking, and decoying (virtual and actual decoys). The resistance to ECM by
RF-based systems can be achieved through secure RF communications using low-
energy transmissions, frequency hopping over a large frequency band,
encryption, pulse coding, and multiple authentication layers.

###  VII-H Laser-Based Techniques

Laser is used for different civilian and defense applications. The laser can
also be used for DCT-U. For example, in [100], a lidar sensor is used for the
detection and tracking of small UAVs. Furthermore, a novel approach using a
mesh of laser beams to detect, track, and classify small UAVs and stealth
aerial vehicles was provided in [254]. At least two airborne platforms at
different spatial positions were required. The two airborne platforms
transmitted laser beams toward the ground RXs such that a mesh was formed as
shown in Fig. 13. If an aerial vehicle blocked the path of the laser beams, it
was detected and subsequently tracked and classified. Laser beam steering was
also suggested to increase the coverage range and for better tracking and
classification in [254].

###  VII-I Distributed Sensors and Sensor Fusion

A network of different types of off-the-shelf sensors, e.g., imaging, and
acoustic sensors, and SDRs can be placed at distributed locations. The
location of the sensors can be electricity and telecommunication towers, tall
buildings, and other infrastructure. The sensors can be connected to a central
network. The data from different sensors can be fused together for better
situational awareness compared to a single sensor. The distributed sensors can
provide a first layer of detection over a large area.

###  VII-J Regulating the UAV Air Traffic

The threat from malicious and non-cooperative UAVs can be minimized by
regulating UAV air traffic. The UAV traffic can be regulated by installing
control towers similar to air traffic control towers. The control towers can
be installed on already available infrastructure, e.g., telecommunication
towers. A control tower should be able to autonomously regulate the UAV
traffic in the vicinity, e.g., by assigning tags to registered UAVs. The tags
will be updated when the UAV approaches the next control tower limit, which
can help to identify unregistered and malicious UAVs. Another solution is by
using Automatic Dependent Surveillance-Broadcast Protocol (ADS-B) protocol for
the management of UAV traffic. ADS-B can provide improved situational
awareness for UAV and civilian airline operators ensuring safe operations.
Moreover, by adopting different security measures for ADS-B [258], ECM can be
minimized. Other future possibilities for UAV traffic management include using
remote ID [259], unmanned aircraft system traffic management (UTM) [260],
advanced air mobility (AAM) [261], and drone corridors [262].

###  VII-K Airborne UAVs

The sensors on airborne vehicles, e.g., radars can provide long-range
detection against aerial threats [90]. However, the operational and
maintenance cost of manned aerial surveillance vehicles is large. A possible
solution is to use sensors onboard UAVs for surveillance. The surveillance
UAVs can be either remotely controlled by a human operator, can work
autonomously using AI, or both, and can fly as a network in a swarm.
Furthermore, tethered UAVs and/or hot air balloons at different locations can
be used for UAV surveillance.