exploited to obtain the probability distribution of the new positions to
perform tracking, similar to Kalman filtering. The concurrent use of two
radars allows improving the accuracy from $46.9$% to $98.6$%.

The “GaitCube” algorithm was proposed in [151]. It utilizes so called gait
data cubes, i.e., 3D joint-feature representations of micro-Doppler and micro-
Range signatures for human recognition purposes. The idea behind this
algorithm is to exploit the radar’s multi-channel capabilities to improve the
recognition accuracy. Their proposed system achieves $96.1$% accuracy with a
single antenna, $98.3$% when using all antennas and an average accuracy of
$79.1$% when tested in an environment not seen at training time.

Akin to the objectives of the above paper, [165] develops a posture estimation
algorithm using DBSCAN to cluster and single out real targets. The authors
generate their dataset by installing the radar on the ceiling, and receiving
data at $10$ frames per second. The data processing model is based on CNNs,
and the CNN network is trained on lying, seated and upright moving postures.
Classification results demonstrate a mean accuracy of $99.1$% and an average
processing time of $0.13$s.

Another work in [156] performs the classification of 7 fitness exercises. CNN
and LSTM neural network models are utilized for the classification task, by
training them on RD, RA, angle-Doppler (AD) and joint-image data. For these
data types, a classification accuracy of $92.08$%, $98.65$%, $97.7$% and
$99.27$% is attained, respectively. In [153], fitness activities were tested
both in offline and also in online scenarios. Classification was performed on
$5$ human activities achieving an accuracy of $93.25$% and $91.52$% for the
offline and online operation modes, respectively. The system was also tested
on multiple locations and the obtained average accuracy is $88.83$%.

Authors of [166, 167] have created a human detection and tracking algorithm by
using two radars simultaneously. In both of these works, Kalman filter and
DBSCAN were used for tracking and identifying the location of the person, and
the synchronization of the radars were carried out in an offline fashion. The
results in [167] show a significant improvement when a two-radar setup was
used with an accuracy of $98.6$% compared to $46.9$% from single-radar setup
in human detection. In [166], radars were placed so that one had a top-view
and the other had side-view angle. This work in addition to prior work
proposes an alarm system and a posture estimation method. An alarm system is
triggered upon a positive evaluation in the change of cluster number, number
of points in the cluster or the center point of the cluster. The posture
estimation is done for standing, sitting and lying poses by analysing the
height of a person at a particular instance and the accuracy of estimation is
from $92.5$% to $93.7$%.

Object Detection

The authors of [185] propose a method called spatial attention fusion (SAF)
for obstacle detection with mmWave radar and vision sensors. A fully connected
one-stage (FCOS) NN is used for the detection of objects. For the training of
this neural network, radar data is converted into radar maps (images) and
during the feature extraction step, the SAF block within the FCOS network is
used for combining radar with vision features. The proposed SAF-FCOS model is
trained and tested on the nuScenes dataset with a ResNet-50 backbone,
achieving an average precision of $90.0$% with an intersection-over-union of
$0.50$ or higher.

The detection of concealed objects implies additional challenges, as it
becomes necessary to single out hidden objects from rest of the scenery. In
[198], the authors employ EM to fit a Gaussian mixture model of the image
acquired: through a two-step image segmentation procedure, they first extract
the body area from the image and then detect concealed objects. The model is
evaluated in terms of average probability of error and the authors report that
multi-level EM has an increased performance of up to $90.0$% when compared to
conventional EM.

A real-time outdoor hidden object detection model is proposed in [199]. This
work also utilizes EM, Bayesian decision making and Gaussian mixture model for
image segmentation, with an architecture similar to that of [198]. However,
vector quantization is adopted for the first segmentation level to achieve
faster computation times. The authors also state that EM can be avoided as a
whole to reduce computation time (and complexity) significantly, but this
causes an error increase as well. As a result, [199] achieves a computation
time of $1.11$ s (with EM) and $0.134$ s (without EM) per frame.

Along a similar line, the authors of [200] propose a writing object (e.g., a
pen) tracking system called “mTrack,” that uses dedicated mechanisms to
suppress interference from background reflections. After this, the RX antenna
is steered according to the peak response observed on the reflected received
signal. In other words, the antenna is adaptively steered to face and track
the direction of the pen. Finally, the target movement tendency is evaluated
by the trend and amount of phase shifting. The system can detect the location
of the pen at a $94$% accuracy, with a tracking error smaller than $10$ mm
across $90$% of the trajectory.

In [201], a non-imaging sensor for hidden object detection is developed. The
authors test their device both in an outdoor scenario with a gun and in an
indoor scenario where plastic sticks of diameter equal to 2 cm are covered by
a fabric. Final results of the model are evaluated by applying Fourier
transform on IF chirps to get the range map on horizontal and vertical scans
of the environment compared with a captured image. In [202], an improved
version of the sensor is proposed, using a horn antenna integrated with a
focusing dielectric lens operating in the 80–100 GHz frequency range. This
sensor can be operated with any preferred movement (e.g., up-down) and the
authors claim that the probability of detection can go up to $100$%.

In [148], an IEEE 802.11ad device is used as a pulsed wave radar to perform
passive handwriting tracking. Slow- and fast-time dimension analysis of the
complex CIR, cell-averaging constant false alarm rate (CA-CFAR) and subsample
peak interpolation (SPI) are the underlying techniques used in their
algorithm. After applying digital beamforming, the authors could extract
Doppler maps and by choosing the bins with higher Doppler power, could
localize the writing tool (a pen). Finally, the pen is tracked by picking the
lowest elevation angle of its lower part at each time-step. With this, they
achieved a tracking accuracy between $3$ mm (at a distance of $20$ cm) and
$40$ mm (at a distance of $3$ m) and a character recognition accuracy ranging
from $72$% to $82$%.

The authors of [203] perform object classification considering three classes:
humans, drones and cars. The feature set used in their algorithm consists of
radial range, area under the peak, width of the peak, height and standard
deviation of the peak in the range-FFT domain. Logistic regression and Naive
Bayes led to a classification accuracy of $86.9$% and $73.9$%, respectively.

In [204], authors have developed a new deep learning model called hybrid
dilated convolution (HDC) for concealed object detection. HDC uses two-class
semantic segmentation network for keeping a high resolution in order to detect
small objects. As a design rule and assignment strategy, expand-contract
dilation (ECD) assignment is applied. In this assignment stage, the first
dilation rate group forms the “rising edge” (increasing dilation) and the rest
forms the “falling edge” (decreasing dilation) of dilation rates. As a result,
their average precision with intersection over union of $0.5$ is at $0.69$%
which outperforms rest of the existing techniques.

Health Monitoring

The authors of [205] propose a model for remote heart rate (HR) monitoring and
analysis. They use the Levenberg-Marquardt (LM) algorithm for the extraction
of heart-rate information. The sum of heartbeat complex, respiration, body
movement, background noise, and electronic system noise is gathered by
expressing the received in-phase and quadrature-phase components from LM as
the cosine and sine of the received signal. One distinctive advantage of this
method is that there is no phase unwrapping as the fitting of the HR signal is
directly carried out on the cosine and sine of the received phase modulated
signal, which is important for low SNR scenarios. The method is able to
estimate beat-to-beat HR and individual heartbeat amplitude, both having a
critical role in the diagnosis of heart diseases.

The authors of [149] demonstrate a remote breathing and sleep position
monitoring system over multiple people at the same time. High resolution AoA
detection is used to identify closely located targets. A support vector
machine (SVM) is used for finding the sleep position, and an optimal filter to
estimate the breathing rate. The designed system achieved an accuracy of $97$%
for breathing rate estimation and of $83$% for sleep position detection.

In the same vein, the work in [147] proposes vital sign and sleep monitoring
system. Initially, the location of the person is detected by using the
reflection loss as the classification parameter, performing a $360$° sweep of
the environment. After localizing the human, reflected signal strength samples
from the reflected signal directed at the human are gathered. For heart rate
detection, FFT, customized band-pass filter, inverse FFT (IFFT) and peak
detection are applied, while for breathing rate detection only IFFT and peak
detection were sufficient. The achieved accuracy was $98.4$% and the mean
estimation error in breathing rate and heart rate estimation for an incident
angle of $70$° was smaller than $0.5$ bpm and $2.5$ bpm, respectively.

A similar purpose is found in [164], which designs a robot for human detection
and heart rate monitoring. The Hungarian Algorithm and Kalman filtering are
used to detect and track the user position. Once the person is located, the
robot approaches him or her and starts the scanning process. The biquad
cascade infinite impulse response (IIR) filter is used to extract the
heartbeat waveforms from the signal, whereas a NN is used for predicting the
heart rate. The proposed system achieved an accuracy between $91.08$% and
$97.89$% across eight different poses.

For the purpose of remote glucose level monitoring, the authors of [144, 143]
observe reflected multi-channel signal signatures collected through the SOLI
mmWave sensor [206]. The signal is analyzed by obtaining average power
spectral density (PSD) of each gated signal vector by applying DFT and FFT.
With this, they were able to sense the change in dielectric constant due to a
varying glucose level in the blood.

The authors of [163] use the radars’ multi-channel capabilities to improve the
estimation of vital signs (heart rate). Experiments are performed on $4$
different scenarios by changing the location of the radar and the posture of
the subject. Authors claim that using multi-channel Maximal Ratio Combining
(MRC) outperforms single channel estimates in most cases, quantifying the
benefits for each scenario.

Other Applications

In [159], a system namely “mmVib” for micrometer level vibration detection is
presented. The authors propose a multi-signal consolidation model to
understand In-phase and Quadrature (IQ) domain and in turn exploit the
consistency among the two obtained signals to estimate the vibration
characteristics of an object. With this, they can detect vibrations at
micrometer level.

The authors of [150] propose an indoor mapping system called “milliMap”,
designed for low-visibility environments. A lidar is used for environment
mapping as a ground truth data collector. A GAN is used to construct the grid
map by recognizing obstacles, free spaces and unknown areas. Finally, semantic
mapping is applied for the classification of obstacles.

A noise-resistant speech sensing framework, “WaveEar,” is proposed in [207].
Directional beamforming is used to make the system robust to noise. After
localizing the throat and receiving the data, voice reconstruction is achieved
by a neural network based on an encoder-decoder (autoencoder) architecture. As
a result, WaveEar achieves a stable $5.5$% word error rate at a distance of
about $1.5$ m from the user. The authors also point out that joint
optimization speech-to-text and WaveEar would further enhance the capabilities
of their system.

In [160], a mmWave radar device is mounted on a robot to estimate its
position. This is achieved by exploiting the interference produced by other
mmWave radars located in the same environment (with known positions), and by
only estimating the angle of arrival of each other radar interference. The
proposed system attains position errors for the robot ranging from $14$ cm
(with three radars) down to $6$ cm (ten radars).

###  VI-G Summary

In this section, we have summarized the recent advances and trends in signal
processing for passive mmWave radar systems for indoor spaces. These systems
are rapidly gaining momentum as radar devices become commercially available,
at a low cost. A number of applications are emerging, targeting diverse
scenarios such as people detection, tracking and identification, estimation of
biosignals such as respiration and heart rate, detection of
gestures/activities/falls, vibrations, speech or environmental mapping. Table
IX summarizes these application-oriented propositions, while Table XI
categorizes them based on the environment where the experiments were carried
out. While early works used standard machine learning algorithms such as
expectation maximization and support vector machines, latest developments have
been dominated by neural networks. This is clearly evident from Table XII,
which presents a summary of the analytical tools discussed in the survey.
These are being implemented in their many flavors, and are allowing
researchers to obtain good results in scenarios where no analytical models are
available. As far as human data monitoring is concerned (e.g., people
tracking, activity monitoring, etc.), the key processing algorithms are DBSCAN
clustering for the separation of user data in the radar RD/RA/RDA maps and
Kalman filtering to reliably track their trajectories. Neural network
architectures are evolving from standard CNNs to more advanced convolutions
(PointConv and PointNets) that were specifically designed for radar point
clouds. Some solutions then use RNNs to capture and exploit the temporal
correlation of radar signals. Advanced architectures, such as GAN based, are
also being exploited to extract features from radar images.

TABLE IX: Summary of the mmWave radar sensing works in the literature Proposition | Tools Used | Band (GHz) | Performance
---|---|---|---
Human Activity Recognition Algorithms
Multi-person tracking and identification [145] |  Micro-Doppler, DBSCAN, Kalman filter, Hungarian algorithm, CNN | 77 |  Continuous identification of multiple persons with up to 98% accuracy.
Multi-person detection and identification [146] |  LSTM-based model, RSS series analysis | 60 |  Multi person classification and identification accuracy of 97.73% and 93% respectively.
Gait-based human recognition [151] |  CNN | 77 |  Classification accuracy of 96.1% and 98.3% with single gait cycle, when using single and all receive antenna respectively.
Human detection and tracking [152] |  DBSCAN, probability distribution matching, Kalman filter-like algorithm | 77-81 |  Human detection sensitivity and precision of 90% and 98.6% respectively.
Real-time human activity recognition [153] |  DBSCAN, CNN, RNN | 77 |  Offline and real-time activity recognition accuracy of 93.25% and 91.52% respectively, over five different human activities.
Hand gesture classification [154] |  Deep learning, Signal processing (FFT) | 77-81 |  Hand gesture classification accuracy of 93% and 95% on range and range-angle data respectively.
Human motion behavior detection [155] |  Micro-Doppler, DBSCAN, CNN | 77 |  Accuracy of over 90% in detecting various human motion behaviours.
Activity recognition and fitness tracker [156] |  Deep learning, CNN | 77-81 |  Classification accuracy of 92.08%, 98.65%, 97.7%, and 99.27% for RD, RA, Angle-Doppler (AD), and joint-image evaluation respectively.
Real-time patient behaviour detection [157] |  micro-Doppler, STFT, CNN | 77 |  Over 84.31% prediction accuracy for different behaviors for a single patient. Around 80% prediction accuracy for different behaviors for two patients.
Human skeletal pose estimation [158] |  CNN | 77 |  Detection of 17 human skeletal joints with 3.2 cm, 2.7 cm and 7.5 cm localization error on depth, elevation, and azimuth axes respectively.
Human pose estimation [161] |  CNN, Fractionally strided CNN | 77 |  Average object keypoints similarity of 70.5 over 8 different parts.
Fall detection system [162] |  LSTM, RNN | 77 |  Proposed scheme achieves 98% fall detection rate and outperforms the baseline techniques.
Real-time posture estimation system [165] |  DBSCAN, CNN, LSTM, Decision trees | 77 |  Posture estimation with an accuracy of 99.1% at a processing time of 0.13s
Human detection and tracking [166], [167] |  DBSCAN, Kalman filters | 76-81 |  Human detection sensitivity of over 90%. Two-radar setup improves precision from 46.9% to 98.6%. Posture estimation precision from $92.5$% to $93.7$%
Object Detection Algorithms
Handwriting tracking [148] |  STFT, CIR, Cell averaging-constant false alarm rate (CA-CFAR) | 60 |  Tracking accuracy of 3 mm to 40 mm and character recognition accuracy of 72% to 82%.
Obstacle detection for autonomous driving [185] |  Deep learning, CNN | 77 |  Average precision of 90% with intersection of unions greater than 0.5.
Concealed object detection [198] |  Gaussian smoothing filter, expectation-maximization, Bayesian | 37.47 |  Usage of multi-level EM increased performance up to 90% compared to conventional EM.
Real-time concealed object detection [199] |  Expectation-Maximization, Bayesian decision making, Gaussian mixture model | 94 |  Computation time of 1.11 s and 0.134 s with reduced processing.
Writing object tracking (mTrack) [200] |  RSS, phase change analysis | 60 |  The system tracks/locates a pen with sub-centimeter accuracy in 90% of the cases.
Concealed object detection [201, 202] |  FT | 80-100 |  Object detection accuracy up to $100$%.
Object classification [203] |  FFT, Logistic regression, Naive Bayes | 77-81 |  86.9% and 73.9% classification accuracy using Logistic Regression and Naive Bayes respectively.
Hidden object detection [204] |  Semantic segmentation, CNN | 60 |  The proposed expand-contract dilation (ECD) scheme has an average precision<EMAIL_ADDRESS>of 0.69, and outperforms all the existing techniques.
Health Monitoring Algorithms
Blood glucose level monitoring [143] |  DFT, FFT | 57-64 |  Remote detection of blood glucose levels by sensing the change in dielectric constant and loss tangent.
Glucose level detection [144] |  DTFT, energy-density comparison | 57-64 |  Demonstrates accurate identification of blood glucose levels.
Vital sign and sleep monitoring [147] |  RSS, IFFT | 60 |  Human finding accuracy of 98.4% and the mean estimation error in breathing rate and heart rate is less then 0.43 Bpm and 2.15 Bpm.
Breathing and sleep position monitoring [149] |  FFT, DOA, optimum filter, SVM | 77-81 |  Accuracy of 97% and 83% for breathing rate estimation and sleep position detection respectively.
Vital sign monitoring [163] |  Arctangent demodulation (AD), Maximal ratio combining (MRC) | 77-81 |  Proposed signal processing chain significantly improves the heart rate estimation accuracy in all cases.
Heart rate sensing [164] |  Neural networks, Hungarian algorithm, Kalman filter | 60-64 |  Accuracy of 91.08–97.89% over 8 different human poses.
Heart rate analysis [205] |  Non-linear Levenberg-Marquardt | 94 |  Capability of estimating beat-to-beat heart rate and individual heartbeat amplitude.
Other Algorithms
Indoor mapping [150] |  GAN | 77 |  Map reconstruction error within 0.2 m. Obstacle classification accuracy of 90%.
Vibration detection [159] |  FFT, AoA | 77 |  Median amplitude error of 3.4 $\mu$m for the 100 $\mu$m amplitude vibration.
Robot position estimation [160] |  AoA, range and doppler FFT | 77 |  Position estimation of the robot with an error below 20 cm.
Speech sensing [207] |  SSNR, Neural network | 77 |  5.5% word error rate around 1.5 m distance
TABLE XI: Summary of the environments in which the experiments have been carried out Evaluation | Related literature
---|---
Indoors | [145, 155, 146, 157, 162, 161, 198, 200, 201, 202, 205, 149, 208, 144, 143, 147, 164, 159, 150, 207, 153, 151, 166, 154, 160, 148, 163, 152, 165, 204, 167]
Outdoors | [158, 161, 185, 199, 201, 202, 203, 156]
TABLE XII: Summary of the main techniques used in the surveyed papers Analytical Tools | Related Literature
---|---
DBSCAN | [145, 155, 166, 152, 165, 153, 167]
Deep learning | [145, 155, 146, 157, 158, 162, 161, 185, 164, 150, 207, 153, 151, 154, 152, 165, 156, 153]
Fourier transform | [201, 144, 143, 147, 159, 154, 160, 148, 203]
Hungarian algorithm | [145, 164]
Kalman filter | [145, 164, 166, 167]
Levenberg-Marquard method | [205]
Machine learning | [149, 165, 203]
Non-max suppression | [161]
Signal processing | [146, 200, 147, 147, 207, 148, 163]
Statistical modeling | [198, 199, 152]

##  VII Discussion and open research directions

Our comprehensive review of the state of the art in mmWave localization and
sensing shows that a sizeable set of contributions have already covered
significant work in this research area. Such works show that current mmWave
equipment, even COTS devices, already offer sufficient opportunities to
incorporate localization as part of communication processes. Moreover,
commercial implementations of mmWave radars are currently very compact, and
cater for precise device-free localization and sensing. However, additional
efforts are required to democratize these tasks and make them natively
available to vertical applications that rely on mmWave connectivity.

At the current stage of hardware development, fully-custom signal processing
algorithms only apply to software-defined radio platforms, where fully-digital
transceiver architectures can be available upfront. Conversely, commercial-
grade hardware does not give full access to internal signal samples and
measurements, requiring more complex processing and often yielding limited
performance. For example, while theoretical analysis predicts millimeter-level
device localization accuracy and fully digital architectures achieve
centimeter-level accuracy, algorithms for commercial-grade mmWave devices
typically achieve decimeter-level 3rd-quartile localization errors.

In this perspective, we conclude that the research in the above field would
greatly benefit from new-generation standard-compliant mmWave transceiver
hardware that also exposes channel state information to external algorithms.
While some efforts in this direction have been announced, there is still no
such platform available on the market. The same observation holds for hybrid
beamforming architectures. While preliminary works exist that exploit these
kinds of architectures to improve beam pattern directivity and adaptivity, or
to make the 802.11ad SLS operations faster, these architectures could also
help localize mmWave devices faster, e.g., by enabling faster angular spectrum
scanning. All of the above would be important enablers of a fully integrated
device-based sensing and localization system, for which significant research
is still needed. The benefits of achieving such a system would be enormous, as
a seamless integration of device-based localization and communications would
enable multiple network optimizations (such as optimal client-AP associations,
predictive re-association before link breakage due to movement or obstacles,
or location-aided beam training and tracking), as well as advanced location-
based services.

As for passive radar sensing, a number of major advancements are envisaged.
First, most commercial low-cost radars incorporate linear antenna arrays,
which have limited detection and tracking capabilities. Bi-dimensional antenna
arrays would make it possible to detect higher resolution radar images in the
3D space, enabling new uses of this technology (e.g., human gait analysis).
Almost no work is available to date exploiting 2D antenna arrays. Second, most
of the available research only involves a single radar sensor, whereas
multiple sensors could be as well co-deployed within the same physical space,
giving rise to new opportunities and technical challenges to face, such as
novel techniques to perform sensor fusion from multiple radar views, self-
calibration algorithms for distributed radar sensors, as well as the
transmission and compression of radar features from multiple sensing points.
Other opportunities concern the combination of mmWave radar systems with
camera-based ones (including thermal cameras), performing data/feature fusion
across different sensing domains. Finally, a promising research avenue is to
modify commercial off-the-shelf communication technology, such as the
forthcoming IEEE 802.11ay, so as to exploit it as a passive mmWave radar. This
would enable joint communications and passive sensing, without having to
deploy a dedicated mmWave radar network.

As a general observation, the research on machine learning methods applied to
device-based localization remains limited compared to device-free radar-based
sensing. For device-based localization, machine learning methods find their
typical application in fingerprinting approaches. Yet, these schemes require a
typically lengthy preliminary measurement effort, which is often deemed
excessive or impractical. Conversely, modern mmWave radar systems are both
compact and affordable, and expose a number of features that can be more
easily passed on to complex learning and clustering algorithms to map
environments, track movement, or estimate the occurrence of some events of
interest. The applicability of machine learning algorithms to to either field
could change if more features become available, e.g, from multiple digital
transceiver architectures integrated in the same client. For example, this
would make it possible to use machine learning to increase the speed of
intermediate localization algorithm steps (e.g., angle computations, ranging
and simultaneous distance estimation among multiple mmWave devices, or joint
angle/distance estimates based on radio features).

##  VIII Conclusions

Millimeter-wave (mmWave)communication devices will soon become a fundamental
component of 5G-and-beyond communication networks. This survey put the lens on
recent research advances in localization and sensing algorithms for indoor
mmWave communication and radar devices. After introducing the most important
properties of mmWave signal propagation and communication chain architectures
that enable mmWave channel measurements, we presented a thorough account of
localization algorithms for mmWave devices. These are based on a broad range
of techniques, that include both traditional methods based, e.g., on timing
and received signal strength indicator (RSSI)information, and more specific
methods that exploit the properties of mmWave devices and signal propagation,
e.g., by processing channel state information (CSI).

Then, we turned our attention to consumer-grade mmWave radar devices, which
are becoming extremely cost-effective sensing platforms. After introducing the
basic structure of such radar architectures, we discussed different approaches
that tackle applications such as human activity recognition, object detection
and health monitoring. We unveiled that several research directions remain
open in both fields, including better algorithms for localization and sensing
with consumer-grade devices, data fusion methods for dense deployments, as
well as an educated application of machine learning methods to both device-
based localization and device-free sensing.

## Acronyms

3GPP

    Third-generation partnership project
4G

    fourth-generation
5G

    fifth-generation
A-BFT

    association beamforming training
ABT

    asymmetric beamforming training
AD

    angle-Doppler
ADC

    analog-to-digital converter
ADoA

    angle difference-of-arrival
AE

    autoencoder
AoA

    angle of arrival
AoD

    angle of departure
AP

    access point
API

    application program interfaces
BI

    beacon interval
BRP

    beam refinement protocol
BS

    base station
CA-CFAR

    cell-averaging constant false alarm rate
CBAP

    contention based access period
CIR

    channel impulse response
CNN

    convolutional NN
COM

    center of mass
COTS

    commercial off-the-shelf
CSI

    channel state information
DAC

    digital-to-analog converter
DBSCAN

    density-based spatial clustering of applications with noise
DFT

    discrete Fourier transform
DKF

    discrete KF
DL

    deep learning
DSP

    digital signal processor
DTI

    data transmission interval
ECD

    expand-contract dilation
EKF

    extended Kalman filter
EM

    expectation maximization
EN-DC

    enhanced UTRA-dual connectivity
ESPRIT

    estimation of signal parameters via rotational invariance techniques
FCOS

    fully connected one-stage
FFT

    fast Fourier transform
FMCW

    frequency-modulated continuous wave
FPGA

    field-programmable gate array
FSCN

    fractionally strided convolutional network
FTM

    fine time measurement
GAN

    generative adversarial network
GRU

    gated recurrent unit
GSCM

    geometry-based stochastic channel model
HDC

    hybrid dilated convolution
HR

    heart rate
HVRAE

    hybrid variational RNN autoencoder
IF

    intermediate-frequency
IFFT

    inverse FFT
IIR

    infinite impulse response
IoT

    Internet of things
ITU-R

    International telecommunication union – radiocommunication Sector
KF

    Kalman filter
LM

    Levenberg-Marquardt
LMS

    least mean squares
LoS

    line-of-sight
LSTM

    long-short term memory
MAC

    medium access control
MCU

    micro-controller unit
MF

    matched filter
MIMO

    multiple-input multiple-output
MISO

    multiple-input single-output
ML

    machine learning
mmWave

    millimeter-wave
MPC

    multipath component
MUSIC

    multiple signal classification
NLoS

    non line-of-sight
NN

    neural network
OKS

    object keypoints similarity
PA

    power amplifier
PDP

    power-delay profile
PHY

    physical layer
PRS

    positioning reference signal
PSD

    power spectral density
PW

    pulsed wave
RA

    range-azimuth
RD

    range-Doppler
RDA

    range-Doppler-azimuth
ResNet

    residual network
RF

    radio frequency
RGB

    red-green-blue
RMS

    root mean square
RMSE

    root mean square error
RNN

    recursive NN
RSS

    received signal strength
RSSI

    received signal strength indicator
RTT

    round-trip time
RX

    receive/receiver
RZF

    regularized zero-forcing
SAF

    spatial attention fusion
SAGE

    space-alternating generalized expectation maximization
SDR

    software-defined radio
SLAM

    simultaneous localization and mapping
SLS

    sector-level sweep
SNR

    signal-to-noise ratio
SoC

    system-on-chip
SP

    service period
SPI

    subsample peak interpolation
SRS

    sounding reference signal
SSW

    sector sweep
STA

    station
STFT

    short-time Fourier transform
SV

    Saleh-Valenzuela
SVM

    support vector machine
TDoA

    time difference-of-arrival
ToA

    time of arrival
ToF

    time of flight
TX

    transmit/transmitter
TXSS

    transmit sweep
UAV

    unmanned aerial vehicle
UE

    user equipment
UKF

    unscented Kalman filter
URLLC

    ultra-reliable low-latency communications
USRP

    universal software radio peripheral
V2X

    vehicle-to-everything
VCO

    voltage-controlled oscillator
WLAN

    wireless LAN
ZF

    zero-forcing

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