# Demonstrating the Merits of Integrating Multipath Signals into 5G LoS-Based
Positioning Systems for Navigation in Challenging Environments

Qamar Bader*  —∗†, Sharief Saleh*  —∗†, Mohamed Elhabiby*  —‡, and Aboelmagd
Noureldin*  —∗†
This work was supported by grants from the Natural Sciences and Engineering
Research Council of Canada (NSERC) under grant number: ALLRP-560898-20 and
RGPIN-2020-03900. ∗ Department of Electrical and Computer Engineering, Queen’s
University, Kingston, ON, Canada
† Navigation and Instrumentation (NavINST) Research Lab,
Department of Electrical and Computer Engineering, Royal Military College of
Canada, Kingston, ON, Canada
‡Micro Engineering Tech. Inc.
Email: {qamar.bader, sharief.saleh<EMAIL_ADDRESS>
###### Abstract

Constrained environments, such as indoor and urban settings, present a
significant challenge for accurate moving object positioning due to the
diminished line-of-sight (LoS)communication with the wireless anchor used for
positioning. The 5th generation new radio (5G NR) millimeter wave (mmWave)
spectrum promises high multipath resolvability in the time and angle domain,
enabling the utilization of multipath signals for such problems rather than
mitigating their effects. This paper investigates the benefits of integrating
multipath signals into 5G LoS-based positioning systems with onboard motion
sensors (OBMS). We provide a comprehensive analysis of the positioning
system’s performance in various conditions of erroneous 5G measurements and
outage scenarios, which offers insights into the system’s behavior in
challenging environments. To validate our approach, we conducted a road test
in downtown Toronto, utilizing actual OBMS measurements gathered from sensors
installed in the test vehicle. The results indicate that utilization of
multipath signals for wireless positioning operating in multipath-rich
environments (e.g. urban and indoor) can bridge 5G LoS signal outages, thus
enhancing the reliability and accuracy of the positioning solution. The
redundant measurements obtained from the multipath signals can enhance the
system’s robustness, particularly when low-cost 5G receivers with a limited
resolution for angle or range measurements are present. This holds true even
when only considering the utilization of single-bounce reflections (SBRs).

###### Index Terms:

5G NR; Autonomous Vehicles (AVs); Kalman Filter (KF); mmWave; Non-line-of-site
(NLoS), Positioning.

††publicationid: pubid:  978-1-6654-3540-6/22 © 2022 IEEE

##  I Introduction

Accurate and reliable positioning of moving objects in indoor environments is
a critical challenge for various applications, such as asset tracking, indoor
navigation, and emergency response. Over the past decade, several indoor
positioning systems have been proposed and implemented, using various
technologies such as Wi-Fi [1], Bluetooth [2], and Ultra-Wideband (UWB) [3].
However, each technology has its limitations in terms of accuracy,
reliability, scalability, and cost [4]. Recently, the emergence of 5G cellular
networks has provided a new opportunity for indoor positioning, thanks to the
support of advanced features such as directional antennas, beamforming, and
multi-antenna arrays [5]. In addition, 5G networks have higher multipath
resolvability, enabling the utilization of multipath signals that were
previously difficult to extract in such environments. This makes 5G a
promising candidate for indoor positioning systems, as it can provide more
accurate and robust positioning solutions. The exploitation of multipath
signals in indoor positioning systems leads to a decrease in 5G signal
outages, thus enhancing the reliability and accuracy of the positioning
solution. Furthermore, the additional measurements provided by the multipath
signals can improve the robustness of the system, particularly in the presence
of low-cost 5G receivers with a limited resolution for angle or range
measurements.

The objective of this work is to examine the advantages of incorporating
multipath signals into 5G-based integrated positioning systems for indoor
environments. The integrated positioning system being analyzed in this paper
is based on our previous work [6], which proposed the integration of 5G
mmWave-based positioning solutions from both LoS and NLoS measurements, along
with onboard motion sensors such as accelerometers, gyroscopes, and an
odometer. To assess the system’s robustness, our investigation will include
analyzing the system under various conditions of erroneous 5G measurements.
Additionally, we will conduct outage analysis by introducing artificial 5G LoS
outages of varying lengths and object dynamics. This study aims to demonstrate
the benefits of incorporating multipath signals in such multipath-rich
environments, and how they can improve the overall robustness and reliability
of the integrated positioning system. Although the used measurements were
acquired from an outdoor dense urban environment, our analysis is equally
important for indoor environments as both environments are filled with
obstacles and multipath-rich.

Our Contribution can be summarised in the following aspects:

  * •

Conducting a comprehensive analysis of the positioning system’s performance
under various conditions of erroneous 5G measurements and outage scenarios,
which provides insights into the system’s behavior in challenging
environments.

  * •

For validation, a road test was conducted in downtown Toronto (Ontario,
Canada) using actual OBMS measurements collected from sensors installed in the
test vehicle. The road tests were carried out in a simulation environment that
accurately emulates the complex urban environment of Toronto’s downtown area.

  * •

Examining the advantages of incorporating multipath signals for positioning in
multipath-rich environments.

The remaining sections of this paper are structured as follows: In Section II,
we present the system overview where we outline the key components of the
positioning system under analysis. Section III describes the experimental
setup used in the road test. Section IV presents the framework for robustness
analysis, along with the results and discussions. Finally, in Section V, we
summarize our conclusions and offer some concluding remarks.

Figure 1: Block diagram of the integrated positioning system under analysis
[6].

##  II System Overview

The evaluated system [6] aims to provide an accurate and reliable positioning
solution for mobile objects in restricted constrained, leveraging a fusion of
5G cellular measurements and OBMS. The system is comprised of three principal
modules, namely, an inertial navigation system (INS) module, a Line-of-Sight
(LoS) positioning module, and a multipath positioning module. A comprehensive
depiction of the overall system architecture is presented in Fig. 1.

The INS module provides the object’s position and velocity estimations
obtained by integrating accelerometer and gyroscope measurements and
additionally provides an estimation of the orientation of the moving object
[7]. The LoS-based positioning module, on the other hand, leverages 5G LoS
measurements such as Round-Trip Time (RTT) and Angle of Departure (AoD) to
estimate the moving object’s position and velocity [8]. The multipath
positioning module leverages reflected signals from obstructions such as
buildings to estimate the position utilizing channel parameters such as time
of arrival (ToA), angle of arrival (AoA), and AoD [9]. Before the positioning
estimation, a measurement exclusion process is performed to filter out Non-
Line-of-Sight (NLoS) signals, allowing only LoS signals to be utilized by the
LoS-based positioning module. This process is based on the distinction in
distance computation between the User Equipment (UE) and the Base Station (BS)
through the utilization of time-based and received signal strength-based
calculations [10].

Furthermore, when multipath signals are used for positioning, channel
parameters are passed to an Out-of-Reflection-Region Identifier (OoRI) module
[11], which filters out higher-order reflections by allowing only single-
bounce reflections to be passed on to the multipath positioning module. The
OoRI module is based on machine learning, trained on a dataset comprising 5G
channel parameters, and achieved a classification accuracy of $99.8\%$. The
position computations resulting from multipath positioning undergo a second
stage of validation, which is contingent upon the vehicle’s motion
constraints. These constraints are determined using odometer measurements and
posterior estimations from the previous epoch. Finally, the position estimates
from the three modules are fused using an unscented Kalman filter (UKF)[12],
which provides a robust and accurate estimate of the mobile object’s position,
velocity, and orientation.

##  III Road Test Setup

For validation purposes, a quasi-real 5G simulation configuration was utilized
from Siradel. As seen in Fig. 2, Siradel’s 5G Channel suite comprises LiDAR-
based maps of downtown city regions like Toronto to display the building
structures, vegetation, and water bodies. As using a map of the actual
downtown area offers accurate information on the physical features of the
environment, it follows that captured multipath signals are likely to resemble
realistic signal propagation. Examples of factors that affect the spread of 5G
signals include building heights, roadway widths, and material compositions.
The simulation tool generates positioning measurables including received
signal strength (RSS), ToA, AoA, and AoD based on the UE reference positions
and virtually connected BS positions by using its ray-tracing capabilities and
propagation models. To collect the UE reference solution, a real car was
equipped by a high-end integrated positioning system provided by Novatel as
seen in Fig. 4. The unit features a tactical-grade IMU (KVH 1750) along with a
GNSS receiver with RTK capabilities. The 3GPP’s Release 16 requirements were
followed, therefore BSs were spaced roughly $250$m apart throughout the
driving path. The required 5G measurables were then acquired by Siradel
utilizing the imported BS positions and the reference solution. The carrier
frequency and bandwidth of Siradel’s mmWave broadcasts were $28$ GHz and $400$
MHz, respectively. The BSs had $8\times 1$ ULAs while the UE had an
omnidirectional antenna.

Figure 2: Downtown Toronto, ON, Google Earth (Top) vs Siradel simulation tool
(Bottom). Figure 3: Downtown Toronto Trajectory (Red), and 5G gNBs (Black
circles). Figure 4: NavINST testbed for multi-sensor positioning.

##  IV Robustness Analysis Results and Discussions

###  IV-A 5G-LoS Outage Analysis

In this section, we present an analysis of the impact of introducing
artificial 5G LoS outages on the 3D positioning accuracy of our proposed
integrated positioning system. We evaluate the performance of the system under
various outage scenarios, with varying duration, distances, speeds, and total
change in azimuth (heading) angle. The objective of this analysis is to assess
the system’s robustness in constrained environments like indoor or urban
environments, where LoS signals are subject to blockages and interruptions due
to the presence of obstacles and other environmental factors. Additionally, we
investigate the effect of single-bounce reflections on the system’s accuracy
by comparing the positioning errors with and without considering these
reflections. Table I presents the characteristics of 10 introduced 5G LoS
outages, while Table II depicts the root mean square error (RMSE) and the max
error as a percentage of distance traveled during a 5G LoS outage.

TABLE I: Artificial 5G-LoS Outages Characteristics Outage # | $\begin{array}[]{c}\textbf{Duration}\\\ {[\mathrm{s}]}\end{array}$ | $\begin{array}[]{c}\textbf{Distance}\\\ {[\mathrm{m}]}\end{array}$ | $\begin{array}[]{c}\textbf{Avg.}\\\ \textbf{Speed}\\\ {[\mathrm{m}/\mathrm{s}]}\end{array}$ | $\begin{array}[]{c}\Delta\\\ \textbf{Azimuth}\\\ {[\mathrm{deg}]}\end{array}$
---|---|---|---|---
1 | 20 | 196 | 9.8 | 72
2 | 20 | 174 | 8.7 | 3.1
3 | 20 | 3.4 | 0.17 | 1.3
4 | 40 | 374 | 9.4 | 152
5 | 40 | 192 | 4.7 | 14.7
6 | 40 | 25.6 | 0.6 | 3.8
7 | 60 | 547 | 9.1 | 156
8 | 200 | 1137 | 5.6 | 300
9 | 400 | 2615 | 6.5 | 674
10 | 1000 | 6255 | 6.3 | 29324
TABLE II: 3D Positioning Errors With and Without SBRs |  Without SBRs |  With SBRs
---|---|---
Outage # | RMS | % | RMS | %
1 | 0.82 | 0.73 | 0.07 | 0.13
2 | 0.90 | 1.10 | 0.04 | 0.11
3 | 0.94 | 40.4 | 0.01 | 0.40
4 | 2.11 | 1.23 | 0.10 | 0.14
5 | 2.15 | 1.86 | 0.03 | 0.11
6 | 0.78 | 6.26 | 0.01 | 0.06
7 | 3.74 | 1.20 | 0.08 | 0.09
8 | 6.32 | 0.79 | 0.10 | 0.07
9 | 9.36 | 0.63 | 0.26 | 0.09
10 | 26.1 | 1.37 | 12.5 | 0.77

The outages’ duration ranges from $20$ seconds to $1000$ seconds, and their
distances range from 3.4 meters to 6255 meters. The average speed of the
object during the outages ranges from $0.17$ m/s to $9.8$ m/s, while the total
cumulative change in azimuth angle ranges from $3.3$ degrees to $29324$
degrees. Overall, it can be seen that the presence of SBRs significantly
reduces the positioning error in all cases. The observation that the
positioning error tends to increase with the duration of the outage, with the
exception of outage 6, warrants further analysis. Several factors may have
contributed to this outcome. Notably, errors in the estimation of the azimuth
angle could lead to inaccuracies in the determination of the object’s
position, which, in turn, is modulated by the object’s speed which is referred
to as non-stationary error [13].

Fig. 5 provides a close-up view of the positioning solution during outage #9,
both before and after incorporating SBRs. The plot demonstrates that, as a
result of the drifting errors of INS, the positioning solution without
multipath assistance drifts away from the reference solution. On the other
hand, the multipath-assisted measurements continue to closely follow the
reference solution.

Figure 5: Close-up scenario of 5G positioning solution during LoS outage
(Shaded area) with and without SBRs.

###  IV-B Effect of Noisy Range and Angle Measurements

In this section, we introduce normally distributed errors in the angle and
range measurements with varying variances to analyze the robustness of the
positioning system before and after incorporating multipath signals. Figure. 6
showcases the cumulative distribution function (CDF) of the positioning
accuracy under noisy range measurements of variances $0.5~{}\text{m}^{2}$,
$1~{}\text{m}^{2}$, and $2~{}\text{m}^{2}$. In general, the utilization of
multipath measurements appears to result in superior positioning accuracy.
Specifically, it has been observed that the positioning accuracy with a
variance of $1~{}\text{m}^{2}$ is consistently higher than that achieved with
a variance of $0.5$ $\text{m}^{2}$ when relying solely on LoS signals.

Figure 6: Positioning CDF due to noisy range measurements.

Figs. 7 and 8 depict the positioning solution prior to and after the
integration of SBRs under the influence of noisy range measurements with a
variance of $2~{}\text{m}^{2}$. It is observed that the presence of noise in
the measurements during the 5G LoS outage, represented by the obstruction of
the BS, leads to large errors in the positioning solution. This can be
potentially attributed to erroneous corrections to the INS mechanization
process, compounded by a complete loss of the LoS measurements. Consequently,
the computed position based on dead reckoning would be subject to substantial
bias until an LoS signal becomes available. The integration with SBRsled to a
notable enhancement in the positioning solution. This improvement can be
attributed to the augmented redundancy of measurements thanks to the increased
availability of SBRs compared to LoS communication. As a result, the solution
was able to track the reference solution more accurately. This highlights the
importance of incorporating redundant measurements, particularly in scenarios
where the primary measurement source, such as the LoS signal, may not be
available.

Figure 7: Close-up scenario of the positioning solution with and without SBRs
under noisy range measurements. Figure 8: Close-up scenario of the positioning
solution with and without SBRs under noisy range measurements.

Fig. 9, in contrast, illustrates the CDF of positioning accuracy affected by
noisy angle measurements. It is worth noting that the computation of position
using multipath signals was impacted by two types of noisy measurements,
namely, AoA and AoD, while only AoA measurements affected the LoS signals.
Despite this, the use of SBRs resulted in superior positioning accuracy.
Examining the sub-$20$ cm level of accuracy, it can be observed that utilizing
multipath signals with a variance of $0.01~{}\text{deg}^{2}$ produced better
positioning accuracy than utilizing LoS signals with a variance of
$0.001~{}\text{deg}^{2}$. Fig. 10 demonstrates the effect of noisy angle
measurements with a variance of $0.05~{}\text{deg}^{2}$ on the positioning
solution. As previously observed in Figs. 7 and 8, the integration of SBRs
successfully reduced the large errors associated with noisy measurements
during LoS outages.

Figure 9: Positioning CDF due to noisy angle measurements. Figure 10: Close-up
scenario of the positioning solution with and without SBRs under noisy angle
measurements.

##  V Conclusion

In conclusion, this study has demonstrated the potential benefits of utilizing
multipath signals in 5G-based indoor positioning systems. The high accuracy of
5G mmWave range and angle measurements, combined with its ability to resolve
multipath signals, presents a promising opportunity for achieving accurate and
reliable indoor positioning. Through our experiments, we have shown that
incorporating multipath-based measurements into our positioning system can
significantly reduce RMS errors during 5G signal outages, improving the
robustness and accuracy of the system. Moreover, the system was also evaluated
under noisy range and angle measurements with varying variances. The results
demonstrate that incorporating multipath measurements leads to improved
positioning accuracy, even when the system is affected by two noisy
measurements instead of one, compared to the system that does not use
multipath assistance. These findings suggest that incorporating multipath
signals into 5G-based indoor positioning systems can enhance their performance
and open up new opportunities for a wide range of applications. Further
research is required to explore the full potential of multipath-based
positioning systems in indoor environments utilizing higher-order reflections.

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