# Bidirectional UWB Localization: A Review on an Elastic Positioning Scheme
for GNSS-deprived Zones

Cung Lian Sang  0000-0002-6144-9544 Michael Adams  0000-0002-4986-3654 Marc
Hesse  0000-0002-9500-3284 Member, IEEE, and Ulrich Rückert  Member, IEEE We
acknowledge support for the publication costs by the Open Access Publication
Fund of Bielefeld University and the Deutsche Forschungsgemeinschaft (DFG).The
authors are with the Cognitronics and Sensor Systems Group, CITEC, Bielefeld
University, Inspiration 1, 33619 Bielefeld, Germany (e-mail:
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>rueckert@techfak.uni-bielefeld.de).
(Corresponding author: Cung Lian Sang)This article has been accepted for
publication in IEEE Journal of Indoor and Seamless Positioning and Navigation.
This is the author’s accepted version which has not been fully edited.
Citation information: DOI 10.1109/JISPIN.2023.3337055

###### Abstract

A bidirectional Ultra-Wideband (UWB) localization scheme is one of the three
widely adopted design integration processes commonly used in time-based UWB
positioning systems. The key property of bidirectional UWB localization is its
ability to serve both navigation and tracking tasks within a single
localization scheme on demand. Traditionally, navigation and tracking in
wireless localization systems were treated as separate entities due to
distinct applicable use-cases and methodological needs in each implementation
process. Therefore, the ability to flexibly or elastically combine two unique
positioning perspectives (navigation and tracking) within a single scheme can
be regarded as a paradigm shift in the way location-based services are
conventionally observed. This article reviews the mentioned bidirectional UWB
localization from the perspective of a flexible and versatile positioning
topology and highlights its potential in the field. In this regard, the
article comprehensively describes the complete system model of the
bidirectional UWB localization scheme using modular processes. It also
discusses the demonstrative evaluation of two system integration processes and
conducts a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis
of the scheme. Furthermore, the prospect of the presented bidirectional
localization scheme for achieving precise location estimation in 5G/6G
wireless mobile networks, as well as in Wi-Fi fine-time measurement-based
positioning systems was briefly discussed.

UWB, bidirectional UWB localization, GNSS-deprived positioning, elastic
positioning scheme, indoor localization, positioning topology, hybrid
positioning, two-way ranging.

##  1 Introduction

In outdoor environments, the Global Navigation Satellite System (GNSS) is
regarded as the de facto standard as well as the industry standard for
positioning and navigation systems [1]. This is because GNSS is capable of
providing the requirements of technical specifications and solutions for many
sectors in governments, industry, and space agencies. However, the GNSS cannot
provide sufficient and reliable location services in indoor and cluttered
environments (e.g. inside buildings, tunnels, underground, underwater, etc.)
[2]. The primary reason is that radio signal propagation in indoor
environments is generally obstructed by obstacles such as buildings, walls,
furniture, etc. Moreover, indoor environments are naturally loaded with
closely located dense objects, which are movable including people as well as
fixed ones. That causes multi-paths and signal blockage situations. As a
consequence, it is infeasible to achieve the direct Line-of-Sight (LOS)
between the mobile device and the satellite base stations, which plays a
central role in the GNSS location estimation process. Therefore, alternative
technologies specifically designed for indoor or GNSS-denied
environments/zones, which is commonly termed as Indoor Positioning Sytem (IPS)
[2], have been profoundly sought in recent years.

Among the available technologies for IPS in the literature [3, 2], Ultra-
Wideband (UWB) technology has been considered a promising and viable option
for GNSS-denied environments [4]. The main reasons for this consideration
include the UWB’s ability to provide decimeter-level (i.e., centimeter-level)
ranging accuracy, penetrate obstacles, support high data rates, potentially
offer low-power and compact hardware, resist jamming, be immune to
interference, and coexist with narrow bandwidth (NB) technologies [5, 6].
Overview, time-based UWB positioning and navigation is a common practice in
the system implementation process. In fact, there are essentially three
topological perspectives on time-based UWB IPS (Section 2). To clarify the
concept of the mentioned three time-based UWB localization systems, we
utilized the well-known GNSS system setup as a contrasting abstract reference
in this article. In the GNSS system setup, in a nutshell, satellites
periodically transmit signals, and the receiver on Earth estimates its
location using those signals to navigate or reach the desired target. In
consideration of this, the setup of UWB-based IPS, particularly for time-based
localization systems, can be broadly categorized into three types [7, 8]
(Section 2):

  1. i)

the GNSS-like UWB scheme, conventionally known as Downlink Time Difference of
Arrival (DL-TDoA) [9] in classical mobile/cellular or satellite positioning
systems,

  2. ii)

the inverted GNSS-like UWB scheme, also known as Uplink Time Difference of
Arrival (UL-TDoA) [9], and

  3. iii)

the hybrid approach or bidirectional UWB scheme that flexibly integrates the
two viewpoints into one topology, conventionally known as a Round Trip Time
(RTT) [10].

In brief, the GNSS-like UWB scheme (i.e., DL-TDoA) is commonly used in
navigation systems, where a moving object is assisted or guided in reaching
its destination by providing position information [11]. In contrast, the
inverted GNSS-like UWB scheme (i.e., UL-TDoA) is typically utilized in
tracking systems, where mobile objects are observed and monitored through a
central server or third party by extracting their location information.
Consequently, the characteristics of navigation and tracking responsibilities
within a positioning system are fundamentally distinct, often requiring
separate implementation processes in practice. The bidirectional UWB scheme
(i.e., RTT) flexibly combines these two perspectives (navigation vs. tracking)
within a single system, making it interchangeable as needed for various
applications.

The topologies of the UWB localization system regarding the GNSS-like scheme
(DL-TDoA) [12] and the inverted GNSS-like scheme (UL-TDoA) [13] have been
extensively explored for industrial and academic use cases in recent years
[14, 15, 16, 17, 22, 18, 19, 20, 21, 23, 24, 25, 26, 27]. However, the
bidirectional UWB localization scheme was typically mentioned as a classical
method in the literature [23, 14, 18, 12, 13], without providing a specific
implementation process, assuming that readers are already familiar with the
concept. In fact, a thorough description of the bidirectional UWB system, from
a theoretical standpoint to practical use cases, is generally lacking
especially for the newcomers. Furthermore, the bidirectional UWB localization
scheme was seldom considered a strategic localization scheme in the literature
[8, 28]. Consequently, the potential of the bidirectional topology in the
field has been excessively overshadowed. Indeed, the bidirectional scheme
possesses several unique properties, applications, and prospects for use in
diverse technologies beyond UWB (Section 2 and 3). Therefore, this article
reviews the aforementioned bidirectional UWB scheme from the perspective of an
elastic positioning topology, and the scheme is dubbed a bidirectional UWB
localization. It aims to introduce the fundamentals of UWB localization
systems with a particular emphasis on bidirectional UWB localization, while
also acknowledging the significance of its unidirectional counterparts. We
attempt the content to be easily understandable and inclusive to a broad
audience interested in UWB, requiring minimum prior knowledge. Accordingly,
this article offers a comprehensive review of bidirectional UWB localization,
covering the foundational concept from the basics (Section 4.1 and 4.2) to its
practical implication (Section 4.3), using a modular design principle
approach. Moreover, the demonstrative evaluation of the measurement results
typically encountered in the practical deployment of UWB technology was also
concisely described (Section 5.1). Likewise, the SWOT analysis of the scheme
compared to its counterpart unidirectional approaches was also discussed in
the article (Section 5.2).

Based upon the perspective mentioned above, the article is organized as
follows: Section 2 explains the details of three topological schemes in the
UWB positioning systems. It is, then, followed by the potential use-cases of
the bidirectional scheme apart from the UWB technology in Section 3. The
system model of the bidirectional UWB localization using the modular design
principle approach was addressed in detail in Section 4 and the demonstrative
evaluation results as well as a SWOT analysis of the scheme were discussed in
Section 5. Thereafter, the related work was addressed in Section 6 and
finally, the concluding remarks were drawn in Section 7.

##  2 Fundamental of UWB Localization Schemes

In terms of the system integration process, localization systems based on
radio wave technology (i.e., positioning using UWB, GNSS, WiFi, Bluetooth Low
Energy (BLE), etc.) can generally be grouped into two namely range-based vs
range-free approaches. In a nutshell, the latter range-free methodology
conventionally relies on algorithms such as fingerprinting or scene analysis
techniques as location estimation methods [4, 29]. In contrast, the former
range-based positioning system first measures the range or distance. This is
accomplished by multiplying a signal time-of-flight (TOF) between a
transmitter and a receiver with the speed of light (i.e., the constant
electromagnetic wave traveling time in the air interface). Then, a dedicated
positioning algorithm is applied to estimate the location of a concerned
moving target based on the measured range. The UWB technology addressed in
this article falls, in general, under the range-based approach.

###  2.1 Brief on Time-based UWB Positioning and Navigation

The localization process of range-based positioning systems can be organized
into two phases, namely: (i) the signal measurement phase (i.e., the ranging
process mentioned above) and (ii) the location estimation phase (i.e.,
estimating a location of a moving target using positioning algorithm and the
measured ranges in the former phase). Measurement in the former phase is
commonly carried out using time of arrival (ToA) [30, 31], TDoA [32], angle of
arrival (AOA) [33, 34] or received signal strength (RSS) [35, 2] techniques.
Particularly for time-based UWB localization [36], the ToA and TDoA techniques
are the classical choice for establishing the system implementation process.
In contrast, the RSS approach is rarely used in UWB localization due to its
poorer performance compared to the other techniques mentioned [8]. Likewise,
the AoA method does not receive as good awareness as its counterpart ToA and
TDoA approaches due to the necessity of a more complex system setup such as
antenna arrays and beamforming techniques [37, 34].

In terms of the location estimation phase, a wide range of algorithms have
been examined in the literature. The most commonly used methods include
Trilateration [30], Multilateration [8, 38], closed-form algebraic methods
like least-square [31], Taylor-series [39], and Maximum likelihood [40], as
well as recursive and statistical positioning techniques like Extended Kalman
Filter (EKF) [41], Unscented Kalman Filter (UKF) [42] and particle filter
[43]. From the perspective of the system implementation process, there are
primarily three topological schemes for the time-based UWB localization
systems (Section 2.2). In short, this article specifically addresses time-
based UWB localization schemes extensively applied in industrial and academic
projects.

###  2.2 The Three Schemes of Time-based UWB Localization

There are mainly three schemes in the implementation process of a time-based
UWB localization system as previously mentioned in Section 1. To the best of
the authors’ knowledge, the earliest definition of the stated three
topological schemes specifically for UWB positioning and navigation was
expressed in [7]. In the mentioned work, the aspect of the UWB localization
system was defined as: (i) a Network-based system (i.e., analogous to the
GNSS-like approach or DL-TDoA), (ii) a Handset-based system (i.e., akin to the
inverted GNSS-like method or UL-TDoA), and (iii) a Hybrid-system (i.e., the
combination of the said two system integration processes namely GNSS-like and
inverted GNSS-like systems). Following the analogous convention specified in
[7], this article categorizes the integration process of time-based UWB
localization system into three topological schemes namely: (i) GNSS-like
unidirectional UWB localization scheme (GUULS), (ii) inverted GNSS-like
unidirectional UWB localization scheme (IGUULS), and (iii) bidirectional UWB
localization scheme (BULS). For clarity, Fig. 1 shows the simplified
diagrammatic illustration of the three UWB localization schemes mentioned in
this article using a single non-stationary mobile device (hereinafter referred
to as tag) and its corresponding four known references or landmarks
(hereinafter referred to as anchors). In a nutshell, anchors are static fixed
devices with their positions known to the system. In contrast, tags are remote
devices commonly placed on mobile objects whose positions are of interest to
determine by the localization system or the location-oriented service
providers. Please note that this article focuses on active or device-based UWB
localization schemes, which require attaching a tag to a mobile device or
subject to determine its location. Device-free or passive UWB localization
schemes [44, 45, 46], where a mobile subject’s location can be estimated
without a specific tag, are not within the scope of this article.

Figure 1: Simplified representation of three time-based topological schemes
for UWB localization: (i) GNSS-like unidirectional UWB localization scheme
(GUULS), a.k.a DL-TDoA, (ii) inverted GNSS-like unidirectional UWB
localization scheme (IGUULS), a.k.a UL-TDoA, and (iii) bidirectional UWB
localization scheme (BULS). The arrows denote the direction of communication
between UWB transceivers.

In a GNSS-like topology denoted as GUULS in this article, the operation of
navigation within an environment can be performed by a tag device typically
attached to a moving object. In GUULS (or DL-TDoA), anchor nodes are placed at
known fixed sites of areas, where the positions of moving remote objects are
considered to be observed. Consequently, the anchors are responsible for
periodically transmitting the UWB signals for the location estimation process
in GUULS. The tag device then listens to the UWB signals sent from the anchors
to estimate its current position to navigate to its targets. The term
unidirectional in GUULS refers to one-way data communication between anchors
and a tag (i.e., the senders are anchors and the receiver is a tag). Thus,
GUULS is analogous to GNSS, where satellites correspond to transmit periodic
radio signals in order to provide the location information of a mobile device
on Earth to navigate the intended target location. The main use-case of GUULS
in location service is the navigation or self-navigation purposes. The signal
direction of data communication for UWB localization using GUULS is expressed
as green dotted arrows in Fig. 1. Regarding the system integration process,
the first GUULS in the context of UWB localization to the best of the authors’
knowledge was described by the work in [12]. Subsequently, the scheme (i.e.,
GUULS or DL-TDoA) has been extensively explored [14, 15, 16, 17, 22, 18, 19,
20, 21]. In the system integration of GUULS, the ranging phase (i.e. measuring
the distances between anchors and tag) is conventionally based on the TDoA
technique [18] whereas the Multilateration method is normally applied for the
location estimation process of the system [47, 15]. It should be noted that
the mentioned ranging and positioning methods are not technically bounded,
though a common practice in the field.

In contrast to GUULS, the inverted GNSS-like system integration process
denoted as IGUULS in this article was widely deployed in the UWB-based
localization systems. In IGUULS or UL-TDoA, tags (i.e., as opposed to anchors
in GUULS) are responsible for transmitting UWB signals for the location
estimation process. Then, the anchor nodes are accountable for computing the
position of the remote moving object by using the sent UWB signals from the
tag. Again, the term unidirectional in IGUULS refers to one-way communication
between UWB transceivers (i.e., the sender is a tag and the receivers are
anchors in IGUULS) for location estimation purposes. The core use case of
IGUULS in location service is a tracking scenario where the central server on
the anchors’ side is capable of simultaneously tracking the motion of all
available tags within its system coverage area. The direction of the signal
for data communication in UWB localization using IGUULS is depicted as red
arrows in Fig. 1. In terms of the integration process for UWB-based
localization, the first IGUULS was proposed and implemented in [13]. Later,
the scheme associated with IGUULS has been thoroughly researched in recent
years [23, 24, 25, 26, 27]. Like GUULS, the TDoA technique for the ranging
phase and the Multilateration method for the location estimation phase is
common practice in IGUULS [13, 24]. From a broad view, the main difference
between the GUULS and IGUULS schemes is the direction of the one-way data
communication flow between anchors and tags (Fig. 1). However, the said
contrast ignites totally distinct usable applications (i.e., tracking vs
navigation) and several contrasting design parameters (Section 2.3).

Unlike the two unidirectional schemes (i.e., GUULS and IGUULS), a
bidirectional strategy for UWB-based localization can also be deployed [8].
The advantage of this approach is that the unique characteristics of the
mentioned unidirectional topologies can be merged together into a single
localization scheme. This enables a promising paradigm shift in the way
location services are traditionally observed from the topological perspective.
This is because the bidirectional localization scheme flexibly allows on-
demand navigation and tracking capabilities for an application without the
need to adjust the system integration process. Otherwise stated, application-
specific demands for navigation or tracking purposes, as well as both
conditions, can be accomplished in a single integration process using a
bidirectional localization scheme. Thus, the ability of BULS to flexibly
and/or elastically behave as a navigator and/or tracker on-demand offers
several unique prospective applications. The term bidirectional in BULS refers
to the two-way UWB signal between the anchors and the tags (Fig. 1). To the
best of the authors’ knowledge, Qorvo (formerly Decawave) initially presented
the demonstration of bidirectional UWB localization identical to BULS in their
commercial UWB development kits dubbed TREK100 Kit (a discontinued product
from the manufacturer) and MDEK1001 Kit [48]. Regarding the MDEK1001 kit,
academic research was also carried out to improve the accuracy of location
estimation of the system in [49]. However, BULS was seldom considered as a
strategic localization scheme as already stated in Section 1, especially in
the form of academic research [8]. Thus, this article highlights the potential
of BULS, addresses its implementation process in detail using modular
processes (Section 4), and discusses the prospective applications of the
bidirectional scheme in other fields of research beyond UWB technology
(Section 3).

###  2.3 Feature Comparison of UWB Localization Schemes

Overall, Table 1 provides the summarized features’ comparison of three UWB
localization schemes discussed in Section 2.2. The bold letter in the Table
indicates a more favorable performance measure in terms of the particular
feature/properties in the table. To clarify the design parameters of the three
time-based UWB localization schemes, the well-known GNSS system setup was
utilized as a contrasting reference as already mentioned in Section 1.
Correspondingly, the unidirectional GUULS system set-up is analogously defined
as the GNSS-like system in this article. By contrast, the IGUULS system setup
is analogous to the inverted GNSS-like system. Finally, the hybrid approach
that combined both GNSS-like and its inverted topological scheme was defined
as BULS, i.e., the bidirectional localization scheme in this article (Table
1).

Table 1: Feature comparison of three time-based localization schemes Properties | Time-based UWB Localization Schemes
---|---
Topology | GUULS | IGUULS | BULS
| [12, 14, 15, 16, 17, 22, 18, 19, 20, 21] | [13, 23, 24, 25, 26, 27] | [8]
System setup |  GNSS-like | inverted | mixture
(GNSS analogy) |  | GNSS-like | of both
Ranging method |  TDoA |  TDoA |  ToA
Clock | mandatory | mandatory | unnecessary
synchronization | at anchors | at anchors |
Localization | navigation | tracking | navigation &
approach | purpose only | purpose only | tracking
Availability of | only at the | only at the | both at the
location data | tag devices | anchor devices | remote &
| (remote side) | (central side) | central sides
Signal direction | unidirectional | unidirectional | bidirectional
System-wide | very low | low | medium
energy usage |  |  |
No. of tags | unlimited | limited | highly limited
No. of anchors | limited | limited | highly limited
Multiplexing | unnecessary | mandatory | mandatory
UWB signal | active | active | active both
transmission | anchors | tags | anchors & tag

In terms of the ranging method (i.e., the distance measurement phase in a
time-based localization system), the two unidirectional schemes (GUULS and
IGUULS) conventionally use the TDoA technique [12, 27]. In principle, the
TDoA-based unidirectional schemes (GUULS and IGUULS) require clock
synchronization between anchor devices due to unavoidable clock drift errors
[14, 23]. On the contrary, clock synchronization is not required in the system
integration process of the ToA-based hybrid approach (i.e., BULS in this
article). This is due to the fact that clock drift errors can be effectively
eliminated with the use of Two-Way Ranging (TWR) method [50, 51]. The ability
to waive clock synchronization in BULS enables the scheme a prospective
natural fit for the location estimation process beyond UWB technology, for
instance, positioning in WiFi fine-time measurement (Section 3).

From the topological perspective, the location information can only be
retrieved on one side in GUULS and IGUULS (Table 1). This is due to the nature
of unidirectional communications (i.e., a one-way UWB signal transmission flow
from a transmitter to a receiver). As a result, location data are accessible
either at the tag sides in GUULS or at the anchor sides in IGUULS. Therefore,
GUULS can be utilized as a navigator or for navigation purposes in location
service-oriented applications [12, 14]. On the other hand, IGUULS can be
utilized as a tracking scenario or a surveillance/monitoring system in
location service-oriented applications [24, 26]. In contrast, BULS exhibits
the capability that can flexibly and/or elastically combine the mentioned two
unique perspectives of localization (i.e., navigation and tracking scenarios)
using just a single scheme [8]. Consequently, in BULS, location information is
available at both the tag and anchor sides. This flexible capability opens up
several potential use cases in location-based services (Section 2.4).

In connection with the system-wide energy consumption, the GUULS (the GNSS-
like topology) is considerably more efficient than the IGUULS (the inverted
GNSS-like topology) and BULS (Table 1). This is due to the fact that only the
anchors are accountable for transmitting the UWB signals. In practice, the
power dissipation (consumption) of a UWB hardware chip in the signal
transmission process is considerably larger than the receiving as well as the
idle processes [47]. In theory, the number of anchors to be used for the UWB
localization system is significantly less than the number of tags in the
system. In contrast, the demand for system-wide power consumption of IGUULS is
getting high as the number of tags in the system increases. This is due to the
fact that tags are responsible for UWB signal transmission in IGUULS [24, 26].
By comparison with GUULS and IGUULS, the system-wide energy consumption of
BULS is the highest among the three time-based UWB topological schemes because
both anchors and tags are involved in the UWB signal transmission for the
location estimation process (Table 1).

Overall, the most notable strength of GUULS is that the number of tags in the
system can be scaled up to the infinity [21, 15, 16, 17] (i.e., there is no
limit for the number of tags in it). Moreover, the multiplexing scheme is
generally not required in the system integration process of GUULS (Table 1).
This empowers the GUULS topology as a simple architecture with a highly
scalable localization system (i.e., unlimited tags). However, the weakness of
GUULS is that it can be used only for navigation purposes (for instance, self-
localization of a robot). This limits the applicable areas of GUULS,
especially in indoor environments. In contrast, the multiplexing scheme is
mandatory for both IGUULS and BULS in order to prevent possible signal
collisions and guarantee the prescribed access time slot for each tag in the
system [26, 8]. The exceptional strength of IGUULS is that the scheme can be
used in the tracking scenario, which has several practical applications
including logistics, industrial automation, safety measures in GNSS-deprived
zones, etc. For that reason, the research on the system integration process of
IGUULS in applications has risen in recent years [13, 23, 24, 25, 26, 27].

The main hurdle in the topology of GUULS and IGUULS is the requirement of a
system-wide clock synchronization (Table 1). On the contrary, the BULS relaxes
the intricate and complex synchronization process by using the TWR method.
Moreover, the use of TWR enables the BULS to behave as a navigator and a
tracker on-demand in applications. Thus, BULS combines the core advantageous
features of GUULS (navigation process) and IGUULS (tracking process) within
one system set-up. The flexible and elastic nature of the BULS opens endless
potential applications as well as a paradigm shift in the way location
services are observed. Moreover, the system integration process of BULS can
easily be transferred into other similar technologies, e.g., Wi-Fi fine time
measurement system defined in IEEE 802.11 mc [54, 53, 52]. This is because TWR
is naturally fitted to the data communication process involved in the said Wi-
Fi system, e.g. the use of Carrier Sense Multiple Access (CSMA) protocol.
However, the BULS also comes with a cost in terms of scalability due to the
need for more ranges in the TWR (Section 4.1.2) as well as the multiplexing
process (Section 4.1.4). Theoretically, the BULS shows a considerably limited
impact in terms of scalability compared to its counterpart the unidirectional
localization schemes (i.e., GUULS and IGUULS).

###  2.4 Potential Use-cases of Bidirectional Localization Scheme

In general, GUULS and IGUULS are useful for large-scale navigation and
tracking application respectively. Besides, the GUULS and IGUULS are
specifically designed for high-time resolution technology like UWB. Thus, the
schemes cannot be easily migrated into application areas in other technologies
such as WiFi, and the 5G/6G mobile networks. One of the major stumbling blocks
is the strict requirement of clock synchronization in the system integration
process for GUULS and IGUULS, as mentioned in the previous section.

By contrast, the BULS can be re-implemented with other time-based wireless
radio technologies without the need to change the core integration procedures.
For instance, one of the prominent candidates regarding such technology
includes the WiFi fine-time technique specified in the recent amendment of
IEEE 80.11mc [53, 54, 52] ( Section 3). In principle, the functional use cases
of the bidirectional localization scheme addressed in this section are not
limited to the UWB technology alone, though it is the primary focus of this
article. Instead, the scheme can be utilized in many other fields, as
discussed a couple of them in the coming Section 3.

For motivational purposes, the prospective use cases of the BULS are
highlighted in this section. To mention a few, the BULS can be used in
navigation and tracking for the underground car parking system, player
tracking and performance analysis in sports, precise location-oriented factory
automation for small to medium sizes, etc. Moreover, BULS can also be used in
mission-critical safety measures, such as precise navigation and tracking
scenarios for firefighters, underground mining, and underwater exploration.
Besides, BULS possesses the potential features for the location estimation
process in 5G and beyond technologies. This is because BULS naturally fits
into the communication protocols utilized in 5G/6G networks, especially for
guaranteed reliability. In addition, the customizable personalized and
dedicated networks established in 5G/6G are promising features for location
estimation services primarily for small and medium-scale factories and other
environments. BULS is a natural fit for the precise location estimation
process in those mentioned areas with the minimum system migration process.

##  3 Viable Prospects of the Bidirectional Localization Scheme Beyond UWB

Though the primary focus of this article is on UWB technology, the
bidirectional localization scheme is not bounded to the UWB-based positioning
systems alone. In fact, the scheme can be applied to other location-oriented
services in different fields. For instance, WiFi Fine Time Measurement (FTM)
was recently adopted in the IEEE 802.11 mc standard [53, 54, 55]. For location
estimation, the distance measuring process of WiFi FTM relies on TWR method
[53, 54, 56, 55]. In general, TWR is considered the core enabler of the
bidirectional scheme as it inherently enables communication flow between two
wireless transceivers by exchanging the perceived data mutually (Section 4.1).
Thus, the bidirectional localization scheme can be considered as a prospective
positioning topology for Wi-Fi FTM in the location estimation processes as
well as the applications enabled by the technology.

Figure 2: Regulation of UWB spectrum masks applied in the US, EU, Japan, and
China [28]. The details of the presented spectrum masks and regulations for
other countries can be observed in [59].

Likewise, the bidirectional localization scheme offers several potential
location-aware applications in 5G and beyond wireless communications. For
instance, the viable scenarios of the bidirectional localization scheme in
5G/6G include: (i) real-time in-networking positioning system for swarm
robotics, (ii) safety aware location estimation in vehicle platooning system,
(iii) real-time positioning system in the internet of drones, (iv)
localization for indoor car parking scenario, (v) self-navigation process for
the driverless transport systems in GNSS-deprived zones, (vi) precise real-
time positioning system for cooperative autonomous vehicles, etc.

In principle, frequency spectrum allocations for 5G and beyond are still under
active discussions and research, especially for the use-case of 5G positioning
system [57]. From the perspective of UWB spectrum allocation, Fig. 2
illustrates the current restriction of UWB usage in terms of the Effective
Isotropic Radiated Power (EIRP) mask regulated by four regulatory bodies,
namely FCC in the US, the European Telecommunications Standards Institute
(ETSI) in EU, Japan, and China. The figure specifically indicates the spectrum
usage of UWB technology, which also collides with the spectrum of interest in
56/6G wireless networks [58]. It is interesting to see that the frequency
spectrums of the common maximum allowable EIRP for the depicted four
regulators are between $7.25\text{\,}\mathrm{GHz}$ and
$8.5\text{\,}\mathrm{GHz}$ (Fig. 2). Except for Japan, the maximum allowable
EIRP is $6.0\text{\,}\mathrm{GHz}$ to $8.5\text{\,}\mathrm{GHz}$. Indeed, the
mentioned spectrum interval of the maximum allowable EIRP is literally the
same for all regulators around the world at the time of writing this article
(see the detailed spectrum restriction of UWB across the globe in [59]). In
particular, the exact value of the maximum allowable EIRP is specified as
$-41.3\text{\,}\mathrm{dBm}$ for UWB worldwide except in China, where it is
specified as $-41.0\text{\,}\mathrm{dBm}$ [59] (Fig. 2).

Regarding this, the 5G and beyond wireless networks are designed for service-
oriented customizable tactical and technological entities and applications
[60]. Therefore, new methodologies of spectrum-sharing schemes are paramount
to the efficient usage of limited resources in 5G/6G [58]. As a consequence,
the adaptation and/or allocation of the currently accessible UWB spectrum into
the 5G/6G network is one of the beneficial approaches to meet the necessities
of some targeted applications, especially for location-oriented solutions in
the areas of industrial and vehicular automation systems. Taking into account
the perspectives expressed in this and previous sections, the system model of
the bidirectional localization scheme based primarily on UWB technology (i.e.
bidirectional UWB localization) is presented in Section 4.

##  4 System Model and the Integration Processes of Bidirectional UWB
Localization

This section elaborates on the system model of BULS, which is also viewed as
an elastic positioning scheme for GNSS-deprived zones in this article. The
modular design principle was taken into account in this section to present the
integration processes of each operational block associated with the BULS (Fig.
3). In other words, the system integration process of BULS was seen as the
combination of several modules, which are independent and can be composed of
different methodologies based on the requirements. Each of the operational
modules is depicted as separate blocks in Fig. 3. In this regard, two system
integration processes were specifically addressed in this article and termed
the minimum system setup for BULS (Section 4.1) and the complimentary system
setup (Section 4.2). In short, BULS can be considered a complete localization
system with minimum requirements if the four operational modules depicted in
Fig. 3 (a) are established. In contrast, the modular blocks colored with light
orange in Fig. 3 (b) are beneficial for the enhancement of the system. Hence,
these modules are recognized as a complementary process and addressed in
Section 4.2.

Figure 3: Block diagram of two system integration processes for bidirectional
UWB localization: (a) a minimum set-up for the system integration process, (b)
a complementary set-up for the system integration process.

The modular approach simplifies the description of the integration procedure
of BULS. However, it should be noted that each functional module preserves its
own standalone and extensible characters composed of different methodologies.
For instance, there are several algorithms [30] that can be used as the
location estimation method in BULS regarding the functional module annotated
as position process (Fig. 3). The same applies to other functional modules
such as the ranging process, multiplexing, identification, etc. In this
article, the detailed applicable methods in each module are ignored for
simplicity. Instead, one methodology for each module is chosen to describe the
integration process. Indeed, the chosen one in the article can be replaced
with other comparable methods by preferences. In light of this, a brief on the
practical implementation process is also discussed in Section 4.3.

###  4.1 Model Integration of BULS with Minimum System Requirements

In general, there are four operational modules mandatory for the model
integration of BULS with minimum setup (Fig. 3 (a)). Those four operational
modules are (i) the UWB hardware components, (ii) the ranging process, (iii)
the location estimation process, and (iv) the multiplexing process of the
system as concisely discussed in subsequent subsections.

####  4.1.1 Typical Hardware Components for UWB Localization Systems

In general, the typical hardware of a UWB device is composed of at least four
main components. The first component corresponds to the antenna of the UWB
device, which is utilized for the transmission and receiving process of UWB
radio signals. The second principal component in the hardware is the UWB chip,
generally produced by a manufacturer. The chip specification is usually
defined in compliance with a standard such as IEEE 802.15.4z [61] (i.e., the
latest amendment to the 802.15.4-2020 standard on the UWB specification at the
time of writing this article). The major tasks of the UWB chip include precise
time-stamping, UWB signal processing, the configuration of the UWB channel,
data rate, emission power, as well as other suitable parameters based on the
intended application. The third principal component of the hardware is the
central processing unit which is responsible for controlling the data
communication flow in the UWB chip. Typically, it is a microcontroller unit in
many applications. The fourth core component is a high-precision oscillator,
whose primary task is to provide an accurate tick to the local clock of the
UWB hardware. The high-precision clock in UWB hardware is paramount for
achieving accurate timestamps used for the ranging process in UWB
localization, especially for the TDoA-based unidirectional approaches, i.e.,
referring to the GUULS and IGUULS discussed in Section 2.3.

On the whole, the underlying hardware components of a UWB device are
principally the same for the three defined time-based topological schemes
(GUULS, IGUULS, and BULS). However, it needs to be cautious that the high
precision oscillators are mandatory for the TDoA-based unidirectional schemes
(GUULS and IGUULS) due to the stringent necessity of clock synchronization in
the system integration (Section 2.3). In contrast, BULS, which is based on the
TWR method, can operate efficiently just by using an ordinary low-cost
oscillator [51]. Today, UWB chips are available at a very low cost in the
electronic markets thanks to the manufacturers like Decawave (at present under
Qorvo), Ubisense, Bespoon, NXP, etc. Moreover, smartphone manufacturers such
as Apple and Samsung have already integrated UWB chips into their products
[62]. It is expected that the exponential growth of UWB chips in the market
and the rise of the technology will be evident in the near future.

####  4.1.2 Ranging Process for Bidirectional UWB Localization

The methodology used in the ranging process basically determines the
applicable localization scheme (i.e., either GUULS, IGUULS, or BULS) in the
system or vice versa. This is because the applicable ranging techniques are
closely tied up with the type of signal measurement processes, i.e., TDoA,
ToA, AoA, or RSS [37] as expressed in Section 2.1. The UWB localization
systems based on the unidirectional approaches (GUULS and IGUULS) are commonly
built upon the TDoA techniques as mentioned before. On the contrary, the UWB
localization system based on the bidirectional approach is fundamentally
established on the ToA technique [8].

Figure 4: Expression of TWR method in the ranging process of the BULS [28].
The image shows both SS-TWR and DS-TWR on an image.

It is noteworthy that the main enabler of BULS stems from the use of a TWR
technique (Fig. 4) in the distance measurement process of the system. The
measured distances using TWR are commonly termed as true range measurements
[8] as opposed to pseudo-ranges in GNSS and alike. This is due to the fact
that TWR is capable of eliminating or reducing clock drift errors [50].
Besides, a recent study in [63] demonstrated the crucial role of TWR in the
system integration process of a plug-and-play UWB-based localization system.
Regarding this, the authors’ previous works [50, 51] revealed that an
Alternative Double-sided TWR (AltDS-TWR) is the most consistent and reliable
ranging method among other available TWRs in the literature. AltDS-TWR was
originally proposed in [64] and we keep the initially given name by the
creators of the method in this article. Accordingly, AltDS-TWR has been later
adopted as the default Double-sided TWR (DS-TWR) technique for range-based
measurements in IEEE 802.15.4z-2020 standard [61]. For more details on AltDS-
TWR as a preferred method compared to other TWR techniques in the literature,
we refer to our previous work [51, 50].

For illustrative purposes, Fig. 4 depicts two basic TWR methods namely SS-TWR
and DS-TWR (i.e., AltDS-TWR in this article) respectively. The SS-TWR (shaded
area in Fig. 4) can be formulated as [50]:

$T_{prop}=\dfrac{1}{2}\cdot(T_{roundA}-T_{replyB})$ (1)

where $T_{prop}$ is the propagation time or TOF of the UWB signal between two
transceivers, $T_{roundA}=\tau_{4}-\tau_{1}$ is the round-trip time of a
signal measured at the local clock of Device A, and
$T_{replyB}=\tau_{3}-\tau_{2}$ is the reply time of a signal measured at the
local clock of Device B.

In the same manner, the formulation of DS-TWR (Fig. 4) specifically AltDS-TWR
(i.e., the name given by the original creator of the method [64]) can be
formulated as [51, 61]:

$T_{prop}=\dfrac{T_{roundA}\cdot T_{roundB}-T_{replyA}\cdot
T_{replyB}}{T_{roundA}+T_{roundB}+T_{replyA}+T_{replyB}}$ (2)

where $T_{roundA}$ and $T_{roundB}$ are the round-trip times of a signal
measured at Device A and B, respectively. $T_{replyA}$ and $T_{replyB}$ are
the reply times or response times measured at Device A and B, respectively.

####  4.1.3 Location Estimation Process

The determination of a target location in BULS is basically based on the
measured distances achieved in the ranging process and a positioning
algorithm. In the UWB localization system, a positioning algorithm requires at
least three ranges to estimate a target location in 2D space (Fig. 5) and at
least four ranges in 3D space (Fig. 6). Normally, eight or more anchors are
utilized for precise location estimation processes in practice, for instance,
a player tracking system in Sports using UWB localization [65]. For brevity
and clarity, this article addresses the fundamental of the lateration-based
positioning method using true-ranges (Fig. 5 and Fig. 6). In short, lateration
is termed Trilateration if the localization system uses exactly three ranges
in the location estimation process. In contrast, it is called Multilateration
if four or more ranges are involved in the estimation process.

Figure 5: Demonstration of the location estimation process in 2D using true-
range measurements [28]. The estimation of the target location is based on:
(a) a single range, (b) two ranges, (c) three ranges, and (d) three imperfect
ranges. Figure 6: Illustration of the location estimation process in 3D
assuming an ideal case with perfect ranges: (a) estimation of the target
location in 3D using two spheres, (b) estimation of the exact target location
in 3D using four spheres.

The necessity of at least three ranges in the 2D setup and at least four in
the 3D can be visualized with a circular and spherical representation as
depicted in Fig. 5 and Fig. 6, especially for the system that used true-ranges
in the signal measurement process. For 2D, the estimation using a single range
alone for a target is anywhere on a circle (Fig. 5 (a)). The feasibility of
the target location is reduced to two points at the intersection of two
circles if two different ranges from two references are utilized in the
estimation process (Fig. 5 (b)). Eventually, the exact position of a target
can be determined in 2D space at the intersection point of three circles by
using three ranges (Fig. 5 (c)). Analogously, the estimation using a single
range in 3D space is a sphere. The use of two ranges minimizes the estimation
of a location into a circle because the intersection between two spheres is a
circle (Fig. 6 (a)). Therefore, at least four ranges are mandatory for
location estimation in 3D space (Fig. 6 (b)).

However, the measured ranges are not ideal in practice and normally affected
by noises such as None-Line-of-Sight (NLOS) conditions and multi-path
scenarios in the propagation of radio signals [66]. In consequence, the
estimation of the target cannot be, in most cases, determined at an exact
single point in practice (e.g., see the 2D case in Fig. 5 (d)). Therefore,
there are several location estimation algorithms for UWB-based localization
systems based on closed-form methods, iterative approaches as well as
statistical techniques [30, 31]. Specifically, our previous work in [30] and
the dissertation in [28] addressed five positioning algorithms commonly used
in UWB localization using real-world experimental data in the evaluations.
Additionally, the associated research data as well as the source codes for the
mentioned research studies were openly provided in [67]. For demonstration
purposes, the positioning algorithm based on the Taylor series technique is
highlighted and explained in this article.

In general, the equation related to true-range measurement in wireless
communication can be defined in 3D space as follows [68] (Fig. 5 and Fig. 6):

$d_{i}^{2}=(x_{i}-x_{t})^{2}+(y_{i}-y_{t})^{2}+(z_{i}-z_{t})^{2}$ (3)

where, $d_{i}$ is the measured distance (range) between the anchor $X_{i}$ and
tag $X_{t}$. In (3), it is assumed that the tag (target device) is located at
position $X_{t}=[x_{t},y_{t},z_{t}]^{T}$ in 3D space whereas the fixed
anchors’ location (the centers of spheres) are at
$X_{i}=[x_{i},y_{i},z_{i}]^{T}$. $i=1,2,...,N$ are the identities of the
anchors.

Based on the spherical ranging equation given in (3)), a function that
corresponds to the $i$th measurement between the $i$th anchor and a tag can be
defined as [39] (Fig. 5 and Fig. 6):

$\displaystyle f_{i}(x,y,z)$ $\displaystyle=$
$\displaystyle\sqrt{(x_{i}-x)^{2}+(y_{i}-y)^{2}+(z_{i}-z)^{2}}$
$\displaystyle=$ $\displaystyle
d_{i}+\varepsilon_{i}~{}~{}~{}~{}~{}~{}~{}(i=1,2,...,n)$

where $\varepsilon_{i}$ is the range estimation error between a tag and the
$i$th anchor. In this regard, it is assumed that the errors ($\varepsilon$)
are statically distributed, and its elements are independent of each other
based on zero-mean Gaussian random variables [39]. Thus, the error covariance
matrix can be written as:

$R=E[\varepsilon\varepsilon^{T}]=diag[\sigma^{2}...\sigma^{2}]$ (5)

where $\sigma$ is the range estimation error.

If we suppose $(x_{v},y_{v},z_{v})$ is an initial guess of the true tag’s
location $(x_{t},y_{t},z_{t})$, we can express as:

$x_{t}=x_{v}+\delta_{x},y_{t}=y_{v}+\delta_{y},z_{t}=z_{v}+\delta_{z}$ (6)

where, $\delta_{x}$, $\delta_{y}$, and $\delta_{z}$ are the location errors
(i.e., the incremental error between the guess and the ground truth or true
position) of a tag to be determined.

If Equation (4.1.3) is expanded into the Taylor-series expansion by keeping
the first-order term, it can be expressed as in the following:

$f_{i,v}+a_{i,1}\cdot\delta_{x}+a_{i,2}\cdot\delta_{y}+a_{i,3}\cdot\delta_{z}\approx
d_{i}+\varepsilon_{i}$ (7)

where,

$f_{i,v}=f_{i}(x_{v},y_{v},z_{v}),~{}~{}a_{i,1}=\frac{\partial f_{i}}{\partial
x}\Bigr{|}_{\begin{subarray}{c}x_{v},y_{v},z_{v}\end{subarray}}=\dfrac{x_{v}-x_{i}}{r_{i}},$
$a_{i,2}=\dfrac{\partial f_{i}}{\partial
y}\Bigr{|}_{\begin{subarray}{c}x_{v},y_{v},z_{v}\end{subarray}}=\dfrac{y_{v}-y_{i}}{r_{i}},a_{i,3}=\frac{\partial
f_{i}}{\partial
z}\Bigr{|}_{\begin{subarray}{c}x_{v},y_{v},z_{v}\end{subarray}}=\dfrac{z_{v}-z_{i}}{r_{i}},$
$r_{i}=\sqrt{(x_{i}-x_{v})^{2}+(y_{i}-y_{v})^{2}+(z_{i}-z_{v})^{2}}$

Equation (7) can be written in matrix notation as

$H\delta=\Delta d+\varepsilon$ (8)

where, $\Delta d=d_{i}-f_{i,v}=d_{i}-r_{i}$,

$H=\begin{bmatrix}a_{1,1}&a_{1,2}&a_{1,3}\\\ a_{2,1}&a_{2,2}&a_{2,3}\\\
&...&\\\
a_{n,1}&a_{n,2}&a_{n,3}\end{bmatrix},\quad\delta=\begin{bmatrix}\delta_{x}\\\
\delta_{y}\\\
\delta_{z}\end{bmatrix},\quad\varepsilon=\begin{bmatrix}\varepsilon_{1}\\\
\varepsilon_{2}\\\ ...\\\ \varepsilon_{n}\end{bmatrix}$

Using the covariance of measurement error ($R$) in (5) as a weight, (8) can be
solved using the overdetermined weighted least squares method [39] as:

$\delta=(H^{T}R^{-1}H)^{-1}H^{T}R^{-1}\Delta d$ (9)

Finally, the location of the target tag device ($x_{t},y_{t},z_{t}$) can be
estimated by using an iterative approach (i.e., continually refining the error
in each process as new data are received in the measurement), if we substitute
the computed incremental error ($\delta$) from (9) to (6).

In summary, the location estimation in the Taylor-series technique starts with
an initial guess. Then, the method improves its estimation at each iteration
step by determining the solution of the local linear least-squares parameters.
The method is categorized under the iterative approach because the beginning
stages of the positioning algorithm will encounter some errors depending on
how well the initial guess comprises the actual ground truth. As the iteration
continues, the estimation of the algorithm will eventually converge to the
pragmatic actual solution. However, it is important that a realistic initial
guess should generally be provided to the system to avoid probable divergence
in iterative approaches.

####  4.1.4 Multiplexing Process

Multiplexing is a method that combines multiple individual outcomes into a
single system output over a shared medium. The primary goal of multiplexing is
to share limited resources within a system by avoiding signal collisions while
achieving efficient processing time. Based on the academic papers and
technical documents published in the literature, there exists only one
multiplexing scheme by far commonly applied in UWB-based localization systems.
That method is a Time Division Multiple Access (TDMA) technique [23, 8, 26]
(Fig. 7). Other multiplexing approaches, such as frequency, code division, and
hybrid techniques, are not utilized especially for the UWB-based system
integration processes. One of the primary reasons is that the TDMA scheme is
very appealing for the high temporal resolution produced by IR-UWB technology
[6].

In short, the TDMA multiplexing scheme is beneficial for a system that
requires power efficiency (e.g., no idle listening) as well as guaranteed
message delivery without collisions (i.e., each node possesses its own time
slot for data processing as depicted in Fig. 4). A single time slot in the
TDMA scheme is generally composed of an active time slot (i.e., the actual
working duration of a node) and a guard time (i.e., a reserved time slot to
compensate for errors/delays due to small variations during processing) as
shown in Fig. 7. The drawbacks of TDMA, however, are the requirement of a
bounded system latency (i.e., a constraint predetermined time which is denoted
as frame duration in Fig. 7) and the waste of resources for unused time slots
in the system. Moreover, network-wide synchronization is mandatory for TDMA in
order to eliminate time-jittering effects, which are a small variation of
time/delay naturally encountered in time-restricted system integration process
[8, 69].

Figure 7: TDMA-based multiplexing scheme with a bounded system latency (i.e.,
frame duration) and individual time slots for each node.

For a system that uses TWR schemes (e.g., BULS), the node synchronization
required for TDMA can be implemented easily. One such algorithm is a timing-
sync protocol originally proposed for wireless sensor networks in [69]. The
fundamental formula for the protocol is as follows (i.e., referred to the
shaded area in Fig. 4 for the illustration of the concept):

$\Delta e=\dfrac{(\tau_{2}-\tau_{1})-(\tau_{4}-\tau_{3})}{2}$ (10)

where $\Delta e$ symbolizes the clock drift between two wireless devices,
$\tau_{1}$ and $\tau_{4}$ are the transmitted and received timestamps of
device A respectively, and $\tau_{3}$ and $\tau_{2}$ are the transmitted and
received timestamps of device B respectively (Fig. 4).

From Equation (10), it is feasible for Device A to correct its own local clock
in order to synchronize with the local clock of device B or vice versa by
adjusting the drift (i.e., $\Delta e$ in Equation (10)) between the two local
devices (refer to the shaded areas in Fig. 4). Suppose the two devices have
been already synchronized to each other (i.e., they are in the same clock
domain), $\Delta e$ in Equation (10) will exactly be zero. Otherwise, $\Delta
e$ in Equation (10) will be either a positive or negative value, i.e.,
depending on the clock of one leading the other during the assessment process
of the mentioned drift $\Delta e$. In terms of the three UWB localization
schemes defined in Section 2.2, the multiplexing technique is applicable to
both IGUULS topology [26, 24] and BULS [8]. In contrast, the multiplexing is
not required for the GUULS (i.e., GNSS-like topology) because the scheme is
capable of providing an unlimited number of mobile devices (tags) for
navigation purposes as already explained in Section 2.3.

###  4.2 Model Integration of BULS with Complementary Processes for System
Enhancement

Referring to Fig. 3 (b), the complementary operational processes of BULS
(i.e., colored with light orange blocks in the figure) are useful mainly for
the enhancement of the positioning system as mentioned in the beginning of
this section (Section 4). Otherwise stated, the bidirectional UWB localization
system will still function properly without these complementary processes.
However, the data measurement process in the real world, especially for radio-
based technologies, is error-prone in nature due to several factors such as
LOS, NLOS, and multi-path conditions as well as the material involved in the
radio propagation path [66]. In consequence, the degradation of location
services is commonly evident in a radio-based positioning system in terms of
performance. Therefore, the complementary processes addressed in this section
play crucial roles in enhancing the location-oriented solutions provided by
the radio-based system. Concretely, the system enhancement for BULS (Fig. 3
(b)) can be carried out by using three methodologies in respect of modular
approach: (i) the filtering process (Section 4.2.1), (ii) the identification
process (Section 4.2.2), and (iii) the mitigation process (Section 4.2.3).
Nevertheless, it should be noted that some modular processes defined in this
section can be mixed or combined into a single process in practice (Section
4.3).

####  4.2.1 State-Space Model for a Filtering Process

The filtering process in the localization system including BULS (Fig. 3 (b))
is a complementary tool for enhancing the overall performance of the system by
efficiently addressing the uncertainty in raw estimation produced by a
positioning algorithm. The primary reason is that no measurement data in
practice are perfect, which is no exception for UWB technology. The filtering
process in the system generally enables the achievement of optimized solutions
for a given task in the aforementioned imperfect measurement environments.
Among others, the Bayesian-based filtering techniques are especially
attractive and broadly applied in radio-based location estimation systems
including the UWB [70]. Moreover, the Bayesian-based filtering process can
flexibly be integrated into the multi-sensor fusion problems depending on
system requirements in addition to the use-case of a typical filter in the
application [71, 72].

Specifically for the UWB-based positioning system, the work in [8, 47, 30, 71,
72] addressed the system implementation processes of the standard Kalman
Filter (KF) as well as its variants such as EKF and UKF, which are basically
built upon the Bayesian framework. In short, the filtering process of BULS in
this article is considered a Bayesian-based state-space model. Though there
are other filtering methods applicable in the location estimation process such
as the Butterworth filter, moving average filter, etc., Kalman-based filters
are the most common and widely used one [73]. Fig. 8 depicted the general
operational flow of standard KF [74], which can be as a filter in the location
estimation process of wireless positioning and navigation for system
enhancement.

Figure 8: Illustration of Standard Kalman Filter Operation Process. The
representation of the image was inspired by the work in [74]

In the Bayesian-based state-space model, the dynamic nature of a system can be
represented with two known mathematical models [74, 75]. The first model is
related to the evolution of the state of a system with respect to time and is
normally termed a dynamic or system model. The second model has associated
with the noisy measurement contributed to the state of the system and is
commonly termed the measurement model. In general, the state and measurement
models can be linear or non-linear based on the scenarios and requirements of
the applications. Specifically for UWB localization, the measurement model is
nonlinear due to the quadratic function in the ranging process as expressed in
Equation (3). However, the state model as a linear function using Newton’s law
of motion is common practice, especially in point-based wireless localization
systems [28]. The point-based system means modeling the moving targets in a
location-based service by assuming point objects (dots in the map) without any
physical shapes, i.e., the estimation is normally based on the center of the
object.

Indeed, there are kinematic/motion models based on linear or nonlinear
mathematical functions depending on the behavior of maneuvers [75]. Though,
the most commonly used motion models for wireless positioning and navigation
systems (i.e., UWB, GNSS, etc.) are the linear ones based on the constant
velocity (CV), a.k.a position velocity as well as white-noise acceleration
model, or the constant acceleration (CA), a.k.a Wiener-sequence acceleration
model. In CV and CA models, it is generally assumed that the derivatives of
the position in the second-order (i.e., velocity for CV) and the third-order
(i.e., acceleration for CA) terms are random processes with zero mean. For
brevity, we refer to the work in [28, 8, 30, 71, 72] for the detailed
implementation of the said state and measurement models for KF in the UWB
localization system.

####  4.2.2 Identification Processes for Non-direct Path Signals in UWB

The majority of errors in the UWB localization system are caused by the non-
direct path signals in the ranging process (Section 4.1.2). This is due to the
fact that the measured ranges in the UWB localization system are basically
affected by the measurement conditions such as LOS, NLOS, and Multi-path (MP).
In this context, the non-direct path signals refer to the NLOS and MP
conditions. In general, the propagation time of the UWB signals is slowed down
when the signal needs to penetrate obstacles (NLOS condition) and it is
elongated in MP conditions [66]. Consequently, the estimated ranges in the
system basically form positive biases in the measurement compared to the
actual true ones. Therefore, the identification process and mitigation process
(Section 4.2.3) of the mentioned errors due to the non-direct path signals are
crucial for enhancing the system’s performance. However, the identification
process of ranging errors in wireless communications (i.e., UWB localization
system in particular) is very challenging and difficult to realize in practice
because of the physical limit in the radio propagation channel [66].

In brief, the conventional techniques regarding the identification process of
non-direct path signal in UWB measurement are mainly based on exploiting the
statistical conditions of the received UWB signal [76, 28]. The commonly used
conventional techniques include statistical methods such as binary hypothesis
test and kurtosis as well as exploiting the information from the received UWB
signal such as signal-to-noise ratio, channel impulse response, received
signal strength, first path signal, etc. [76]. Lately, machine learning
techniques are regarded as attractive solutions for overcoming the mentioned
fundamental limits in wireless communications [77, 78]. In the context of the
UWB localization system, this implies the identification and mitigation
processes of the errors produced by the non-direct path signals. However, the
field is generally considered in its earlier stage with open research.

In general, the identification process of non-direct path signal in UWB
localization is commonly regarded as a binary classification problem (LOS vs.
NLOS) in the literature. For instance, the identification of NLOS scenario was
addressed as a binary class using the conventional technique in [79], the
classical machine learning methods in [80], and deep learning approaches in
[81]. On the contrary, a few works also addressed the mentioned problem as a
multi-class problem [76, 82, 83], from which the research data and its
corresponding source code from [76] were publicly provided as open access.

Figure 9: Illustration of the implementation process for a simple and
effective mitigation technique for UWB localization [28].

####  4.2.3 Mitigation process for non-direct path signals in UWB

This section presents a simple, powerful, and effective technique as well as a
generic one for the mitigation of non-direct path signals in UWB localization
as a modular process (Fig. 3 (b)). In brief, the core idea of the said
mitigation technique is to generate a weighting matrix based on the determined
non-direct path signals in the identification process addressed in the
previous section (Section 4.2.2). Then, the location estimation algorithm
(e.g., Taylor-series method) makes a correction on the errors during
measurements using the generated weighting matrix. The pseudo-code regarding
the weighting matrix generation process for the mitigation of the non-direct
path signals in the UWB ranging phase can be defined as in Algorithm 1 [30,
28]. The presented algorithm shows the generation of a weighting matrix for a
multi-class problem (i.e., identification and mitigation of LOS, NLOS, and MP
conditions in UWB localization).

0: #mp {number of MP measurements in ranging phase}

0: #nl {number of NLOS measurements}

0: #r {total number of the measurements}

1: iw $\Leftarrow$ $\dfrac{1}{\text{\\#r}}$ {initial equal weight value,
scalar}

2: W $\Leftarrow$ eye(#r) {initial weighting matrix}

3: if #nl = 0 and #mp=0 then

4: return W {no weighting is required}

5: else

6: for $i\Leftarrow$ 1 to #r do

7: if measurement $i$ is NLOS then

8: W($i,i$) $\Leftarrow\dfrac{\text{iw}}{\text{2$\cdot$\\#r}}$ {relegate the
initial scalar weight with a weighting factor of $\dfrac{1}{\text{2 $\cdot$
\\#r}}$, which is defined for NLOS condition in this particular use-case}

9: else if measurement $i$ is MP then

10: W($i,i$) $\Leftarrow\dfrac{\text{iw}}{\text{\\#r}}$ { here, the weighting
factor for MP condition is predefined as $\dfrac{1}{\text{\\#r}}$}

11: else

12:
W($i,i$)$\Leftarrow\text{iw}+\dfrac{(\text{iw}-\dfrac{\text{iw}}{\text{$2$\\#r}})\text{\\#nl}+(\text{iw}-\dfrac{\text{iw}}{\text{\\#r}})\text{\\#mp}}{\text{\\#r}-\text{\\#nl}-\text{\\#mp}}$
{distribute the surplus from the relegated weights achieved from NLOS and MP
to the LOS}

13: end if

14: end for

15: end if

16: return W

Algorithm 1 Pseudo-code for generating weighting matrix for mitigation of non-
direct path signals in UWB (multi-classes)

Firstly, the algorithm assigns equal weights to all the identifiable measured
ranges (line $1$ in Algorithm 1). For instance, there are three identifiable
ranges namely LOS, NLOS, and MP in Algorithm 1. The weight of a given UWB
measured range is relegated from its initially given value using a predefined
weighting factor if NLOS or MP conditions are identified in the measurement.
The mentioned exemplary weighting factors are $1/(2\cdot\\#r)$ for NLOS and
$1/\\#r$ for MP condition respectively, i.e., line $8$ and $10$ in Algorithm
1. Consequently, the surpluses (excess) of the two relegated weights from NLOS
and MP are evenly distributed to the weight of LOS conditions within the
measurements (line $12$ in Algorithm 1). It can be shown in the algorithm that
the trace of the weighting matrix (i.e., the sum of all elements in the
diagonal of the matrix) is always $1$ except for extreme cases where all
measurements fall under one condition (i.e., LOS, NLOS, or MP). In such
exceptional cases, equal weights will be applied to the location estimation
algorithm, which has no effect on the measurements. In addition, the mentioned
weighting matrix is commonly assumed as a diagonal matrix because the
measurements in the ranging process are generally independent of each other.
It should be noted that the predefined weighting factors for NLOS and MP
conditions can be tuned in accordance with the system requirements.

The presented mitigation technique can flexibly be applied in the vast
majority of the location estimation algorithms [30, 31] commonly used in UWB
positioning and navigation. The details of positioning methods in UWB
localization as well as the implementation process of the mentioned mitigation
technique on each method are beyond the scope of this article (Section 5.1).
Nevertheless, interested readers are encouraged to look at the comprehensive
detail of such an integration process in [28, 85, 84]. Instead, the exemplary
implementation process of the said mitigation technique on the Taylor series
positioning method (Section 4.1.3) is briefly demonstrated in this article.
Fig. 9 depicts the overview of the integration process for the mentioned
mitigation technique using block diagrams. In a nutshell, the weighting matrix
assigns priority levels in the measurements when location estimation is
conducted, i.e., more weight to the LOS and less to the NLOS and MP
conditions. As a result, the negative impacts of the non-direct path signals
in the system are nicely filtered out to provide the optimized solution. For
the demonstration purpose, the implementation process of the mitigation
technique in the Taylor series-based location estimation algorithm can be
established as in the following by modifying Equation (8):

$\displaystyle H\delta$ $\displaystyle=\Delta d\IEEEnonumber$ (11)
$\displaystyle WH\delta$ $\displaystyle=W\Delta d\IEEEnonumber$ (12)
$\displaystyle\delta$
$\displaystyle=(H^{T}W^{T}WH)^{-1}H^{T}W^{T}Wb\IEEEnonumber$ (13)
$\displaystyle\delta$ $\displaystyle=(H^{T}CH)^{-1}H^{T}Cb$ (14)

where, $W$ is again the weighting matrix generated by the mitigation algorithm
addressed in this section, and $C$ is $W^{T}W$. Suppose that the weight
produced from the inverse of the covariance matrix using the over-determined
least squares (Equation (9)) plays important role in the system, the optimized
solution using the presented weighting matrix is still possible. In that
specific case, the inverse of the covariance matrix needs to be modified in
Equation (9), i.e., $W^{T}R^{-1}W$.

###  4.3 Digest on Implementation Processes in Practice

The primary challenge for the quality assurance of radio-based location
service, i.e., UWB localization in this article, is to deal with the errors
produced by the non-direct path signal propagation as mentioned earlier. The
main reason is that the overall system performance can drastically deteriorate
due to these effects. Thus, the identification and mitigation processes of the
non-direct path signals (NLOS and MP incidents) are vital for the enhancement
of location services provided by wireless positioning systems, particularly in
the UWB localization system. Accordingly, there are several strategic
approaches to overcome the mentioned issues in practice.

In general, the strategy for correcting the ranging errors due to the non-
direct path signals is commonly considered in two steps [80, 86, 87, 85, 84,
89, 88], i.e., like the modular processes addressed in the previous section.
In this regard, the first step involves the identification process as in [84,
89], and the second step is associated with the mitigation process as in [84,
80].

In contrast, there exist strategic approaches that handled both the
identification and mitigation processes of the mentioned non-direct path
signals in one shot or a single process [87, 88, 90, 49]. Basically, the
mentioned one-shot approaches are commonly built upon machine learning or deep
learning structures or framework. In consequence, the application area of the
localization system is usually limited to a few compatible domains as the
models achieved from the mentioned approach cannot easily be transferred or
transformed to other areas. In other words, the results achieved from one
environment using a one-shot approach cannot generally be reproduced with
comparable outcomes in other environments [76]. Hence, the tedious data
collection process, training and validation of the machine learning models for
a specific application, and re-implementation of the system are basically
necessary when the localization scheme based on the one-shot approach is
integrated into each unique environment. The main hurdle of this cause is due
to the unavailability of the datasets yet, which are general enough to cover
many real-world aspects of indoor environments or GNSS-deprived zones. Another
reason is that the unforeseeable uncertainty (i.e., the scenario unobserved in
the collected training and validation data) is commonly encountered in
practice during signal measurement processes in wireless communications.
Whenever uncertainties are involved in the prediction of a machine learning
model, caution should be taken into the integration process as the system
performance can acutely deteriorate, unlike the stable world principles with
well-defined rules such as Chess, Go-the board game, etc. [91].

Theoretically, several modular aspects (e.g., ranging, positioning, filtering,
identification, and mitigation processes, etc.) can be combined into a single
process using machine learning-based one-shot approaches or traditional
localization algorithms. For instance, three modular processes of the UWB
localization system namely the ranging, positioning, and filtering aspect can
be considered as a single process in practice by using the Bayesian-based
location estimation methods like EKF [23], UKF [47, 28], and Particle filter
[43]. Here, the inputs of the localization systems are conventionally the
ranging data and the outputs are the filtered location data estimated by the
applied Bayesian-based positioning algorithm.

Alternatively, a simple select-and-proceed approach can also be established in
UWB localization systems. Fundamentally, a location estimation algorithm needs
at least three ranges in 2D and four ranges in 3D to estimate the position of
a mobile tag as previously explained in Section 4.1.3. However, multiple
anchors are commonly used in the practical implementation process. For
instance, the work in [65] used eight anchors in the system setup whereas
Kinexon’s local positioning system, i.e., an UWB-based sport analytic system
currently deployed in the Handball-Bundesliga league in Germany as well as the
NBA basketball league in North America, required $14$ anchors in the system
setup [92, 93]. In the mentioned systems, it is feasible to select the best
candidates among the extracted available measurements in the ranging process
using classic statistical techniques or machine learning-based approaches.
Then, the location estimation of the interested tag is conducted with a few
chosen ones by a positioning algorithm and the rest of the measurements can
simply be discarded or put aside.

##  5 Demonstrative Evaluation Results and SWOT Analysis

This section concisely demonstrates the comparative analysis of two system
integration processes (i.e., minimum model vs. complementary model) of BULS
using the experimental data (Section 5.1). In addition, the SWOT analysis of
the scheme was also discussed in Section 5.2.

###  5.1 Experimental Demonstration of BULS based on Two Model Integration
Processes

For demonstrative analysis based on real-world measurements, we evaluated the
integration of BULS based on two system models, specifically the minimum setup
(Section 4.1) versus the complementary setup (Section 4.2), using experimental
data. In this context, we deployed four anchors within an indoor sports hall
for the BULS system setup. The experiment involved tracking the location of a
runner (player) within the indoor sports hall, with the boundary of a
basketball court (measuring 28m x 15m) serving as the ground truth reference
for our evaluation. We utilized this boundary as our reference for both Line-
of-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios to establish ground
truth, as it allowed the player under test to easily follow these lines for
our assessment. For the UWB hardware in the setup, we used the evaluation kit
EVK1000 modules from Qorvo (formerly Decawave). During the measurement
process, the ranging data between the tag (i.e. the player) and four anchors
was collected in two scenarios: LOS and NLOS. Then the presented location data
of the player was calculated using first-order Taylor Series (Section 4.1.3)
as a positioning technique [30, 67]. In the LOS scenario, the runner placed
the tag device on top of his head to ensure that direct LOS UWB signals were
received from the tag to all four anchors. Conversely, in the NLOS scenario,
the tag device was positioned in front of the runner’s chest during the data
collection process. The evaluation results for the LOS scenario were presented
in Fig.10, while the results for the NLOS scenario are described in Fig.11.

Figure 10: Demonstration of the two system models’ integration (minimum setup
vs. complementary processes) in LOS scenario. The naming of four anchors
denotes Anc. 1, 2, 3, and 4 in this figure and Fig. 11, starting from the
bottom left counterclockwise. Figure 11: Demonstration of two system models’
integration in NLOS scenario: (a) Comparison of the two system models in terms
of the measured positioning data, (b) comparison of the measured four ranging
data (a tag to four anchors). The annotation of the anchors’ number was
counted in counter-clockwise starting from the bottom left in (a).

In the LOS scenario, the evaluation results showed that there were no
differences between the outcomes produced by the minimum system model and the
complementary model (Fig. 10). In other words, the results revealed that the
minimum and complementary models were able to precisely track the position of
a runner in accordance with the trajectory of the reference (Fig. 10).
Moreover, the two models gave an identical performance in terms of accurately
following the trajectory of the reference in the LOS scenario.

On the contrary, Fig. 11 (a) illustrated the comparative results of two system
models integration processes (minimum vs. complementary) based on the measured
data in the NLOS scenario. Correspondingly, the measured ranging data (a tag
to four anchors) concerning the NLOS scenario was also described in Fig. 11
(b). As expected, the minimum system model cannot correctly follow the
trajectory provided by the reference (Fig. 11 (a)). Moreover, the results
showed that the highly unreliable and chaotic location data are evident in the
minimum system model (i.e., a solid line with orange color in Fig. 11 (a)).
The cause of the mentioned chaotic nature of location data provided by the
minimum system is due to the errors caused by the NLOS conditions in the
ranging phase. This can be confirmed in the measured ranging data provided in
Fig. 11 (b), i.e., the spikes shown in the measurement. In particular, the
higher the spikes in the ranging process, the greater the error in the
location estimation process. Therefore, effectively addressing the mentioned
ranging error by using the identification and mitigation techniques as
addressed in this article is essential for the persistent location information
services in many applications. To demonstrate this, the results achieved by
the complementary model were also provided in Fig. 11 (a), i.e., the dotted
line with green color. It can be stated with confidence that the location data
provided by the complementary model are by far better than the minimum model.
However, the results also disclosed that a perfect trajectory as in the LOS
scenario cannot be achieved in the complementary model, particularly for the
setup and measurements gathered in this specific scenario. This is because at
least three perfect ranges in 2D are required in the location estimation
processes (Section 4.1.3). Consequently, it is relatively hard to achieve
three perfect ranges using four anchors even if the mitigation process is
applied in the given experimental setup (Fig. 11 (b)). Thus, multiple anchors
in the system design are generally the preferred approach in many practical
applications (Section 4.3).

In summary, the primary objective of this section is to offer insights from
experimental real-world measurements, particularly with regard to BULS, in a
concise manner. Concerning this, we conducted an assessment of two BULS system
models: the minimum setup (Section 4.1) and the complementary setup (Section
4.2) within an indoor sports hall using the boundary of a basketball court as
our reference. While both models performed well in LOS conditions (Fig. 10),
the NLOS scenario revealed notable distinctions (Fig. 11). The minimum model
encountered challenges, resulting in unreliable location data due to NLOS-
induced errors. In contrast, the complementary model demonstrated improved
outcomes but was unable to achieve a flawless trajectory. These illustrative
findings emphasize that real-world measurements using wireless communications,
specifically UWB as discussed in this article, can be inherently complex and
may still yield location errors, even when employing techniques like
filtering, identification, and mitigation processes. This highlights the
significance of employing multiple anchors, which is a common practice in
real-world deployments, to mitigate ranging errors and enhance location
accuracy.

###  5.2 SWOT Analysis on the Bidirectional Localization Scheme

SWOT (Strengths, weaknesses, opportunities, and threats) analysis is a tool
commonly used for evaluating technology, business, or the performance of an
organization. The goal of SWOT analysis is to identify the inhibitors and
enhancers of the organization or technology based on internal and external
factors [94]. In this regard, strengths are enhancers of internal factors
(i.e., insiders’ impact) whereas weaknesses are the inhibitors. By contrast,
opportunities are enhancers of external factors (i.e., outsiders’ impact)
whereas threats are inhibitors. In this article, we applied the SWOT analysis
for the assessment of the bidirectional localization scheme. Fig. 12 describes
the summary of the SWOT analysis regarding BULS for wireless positioning and
navigation. Though SWOT analysis is not a quantitative methodology, it is very
helpful for assessing the actual standing of technology or organization (here,
it is a scheme) and for revealing its potential as well as the areas where
effective resolve for a solution is necessary [4].

Figure 12: Summary of SWOT analysis for the bidirectional localization scheme
in wireless positioning and navigation systems.

####  5.2.1 Strengths

The core advantage of BULS is its ability to flexibly and/or elastically
combine two unique perspectives of wireless localization (i.e., navigation vs.
tracking) using a single localization scheme (Section 2.3). This enables the
location information to be extracted in both the tag (a mobile object) and
anchors (a fixed infrastructure). Moreover, the clock synchronization between
UWB transceivers in the system setup, i.e., the main hurdle in GUULS and
IGUULS topologies, is not necessary for BULS. This effectively eliminates the
resources and sophisticated system setup required for the synchronization
process. Thus, plug-and-play system integration can be established in BULS
akin to the work presented in [63]. In addition, the location outcomes
provided by the BULS are generally more accurate than its counterpart
unidirectional approaches (GUULS and IGUULS) because synchronization errors
cannot be completely eliminated in practice [26, 25]. In accordance with the
mentioned strengths and the elastic nature of the scheme, BULS opens endless
potential applications, especially in diversified GNSS-denied environments.
Moreover, the scheme can also be seen as a paradigm shift in the way location
services are observed as mentioned several times in this article. Therefore,
the scheme is suitable for general-purpose precise location services targeted
for both navigation and tracking in one shot. The mentioned feature is very
attractive for mass production and marketing strategy of location-aware
products.

####  5.2.2 Weaknesses

Despite the several advantages of BULS, the scheme does have several
weaknesses as well. The most obvious one is the scalability of the scheme in
terms of available tags and anchors within the system compared to its rival
unidirectional topologies namely GUULS and IGUULS (Section 2.3). Moreover, the
system-wide energy consumption of the scheme is also relatively higher than
its opponents (GUULS and IGUULS) as already expressed in Section 2.3. Another
shortcoming of the scheme is that BULS, i.e., IGUULS as well, needs a
multiplexing scheme in order to avoid possible signal collisions (Section
4.1.4). This leads to the limitation in terms of the volume of tags observable
in the system for BULS and IGUULS. In contrast, GUULS is empowered by a simple
system architecture without the need to use a multiplexing scheme as well as
exceptional scalability in terms of observable tags in the system (i.e., there
is no limitation for the number of tags in this scheme).

####  5.2.3 Opportunities

The bidirectional localization scheme retains several potentials to be used as
a prospective positioning topology for location-oriented services in many
radio-based technologies. A good example is the WiFi FTM defined by IEEE
802.11mc standard, where TWR is intended to enforce in the ranging process as
mentioned earlier in Section 3. Moreover, the bidirectional localization
scheme offers several demanding characteristics for realizing precise and
customizable location-aware services in 5G and beyond wireless networks.
Besides, the present allowable spectrum usage of UWB technology (Fig. 2)
generally collides with the spectrum of interest in 5G/6G wireless networks,
which is still under active research and discussion to date (Section 3).
Therefore, the bidirectional localization scheme can play a significant role
in many applications in 5G/6G-based precise positioning and navigation
systems. In addition, the bidirectional localization scheme naturally fits as
a positioning system for any wireless networks that use the signal
acknowledgment protocol in the data communication process. Here, the message
related to the acknowledgment can generally be made use of as a TWR procedure.
Hypothetically, nearly all protocols in wireless networks use a signal
acknowledgment method for secure and reliable communication flows between
network devices.

####  5.2.4 Threats

The major challenge that prevents the boom of UWB technology (i.e., regardless
of the topology) rapidly in consumer applications may include the distinct
regulations defined worldwide. For instance, the frequency spectrum of the UWB
that permits the maximum allowable EIRP worldwide (i.e.,
$-41.3\text{\,}\mathrm{dBm}$ except in China, where it is
$-41.0\text{\,}\mathrm{dBm}$) is only between $7.25\text{\,}\mathrm{GHz}$ and
$8.5\text{\,}\mathrm{GHz}$ as described before in Section 3 and in Fig. 2. The
rest of the spectrum is sharply diversified based on the country or region
(see the detail about the regulation for other countries or regions in [59]).
The mentioned distinct restriction of UWB spectrum usage in various countries
is, indeed, a stumbling block to the mass production of UWB chips for the
manufacturer as well as for the service providers. Moreover, the
interoperation or co-existence of UWB with narrowband technologies is commonly
separated by restricting the allowable EIRP of the UWB. This may raise a
significant interoperation issue in cases like the use of a device that was
bought from a different country or region. The concern is more intense for the
integration of UWB chips on everyday consumer electronics like Smartphones,
which are normally carried as belongings during traveling or business trips.
This leads to the next common threat generally encountered in the major shift
in technologies, i.e. the public adaption of the technology (i.e., UWB in this
article) as day-to-day consumable electronic products.

##  6 Related Work

As previously discussed, UWB technology has become a popular choice for
precise location estimation in GNSS-deprived positioning and navigation
systems mainly due to its high-accuracy localization capability, i.e.,
typically at around $10\text{\,}\mathrm{cm}20\text{\,}\mathrm{cm}$ [51, 30].
Many surveys and reviews in the literature have explored various aspects of
UWB technology, including its theories, techniques, the technology itself, its
applications, and its future perspective [36, 32, 96, 95, 4, 97, 22]. However,
those surveys and reviews tend to address a wide range of topics, attempting
to cover various methodologies and techniques, often presenting them in brief
paragraphs. As a result, they often overlook in-depth studies, especially
concerning less common topologies and techniques, as well as those that are
considered conventional. For instance, the surveys in [96, 4, 97] and the
review in [95, 22] provide a broad overview of methodological, algorithmic,
and implementation aspects of UWB technology. However, they lack detailed
insights into each presented method, which may leave interested readers in
need of further extensive study.

Furthermore, literature reviews are typically conducted within a defined
topic. In the study of [95], a comprehensive review of UWB technology was
performed, encompassing all radio-based technologies in IPS. Conversely, the
authors in [22] conducted a specific literature review on UWB-based precise
positioning, focusing on its application in smart logistics, by providing a
comprehensive survey of academic literature in this field. Similarly, the
study in [96] focuses on a literature survey of UWB localization, particularly
for the automatic positioning system of anchor nodes, also referred to as
self-calibration or self-localization of UWB anchors. The primary goal of
automatically positioning these anchors in UWB technology is to reduce
deployment costs where manual measurement and tuning processes are labor-
intensive and error-prone [96, 98]. A recent study [98] delves into anchor
self-localization for multi-hop scenarios, as opposed to the more common
single-hop scenarios where UWB signals from tags can reach all anchors, or
vice versa. Differing from the approaches mentioned earlier, this article
offers a comprehensive review of a specific UWB topology or localization
scheme. This scheme is often considered a conventional method, yet its impact
and implications are not adequately described in many papers in the
literature.

As previously discussed in Section 2, time-based localization schemes are
widely utilized in practical UWB deployment, with a particular focus on
measurement techniques based on ToA and TDoA. In recent years, there has been
a vast exploration of time-based UWB localization strategy related to the
GNSS-like schemes denoted as GUULS in this article (aka DL-TDoA) [12, 14, 15,
16, 17, 22, 18, 19, 20, 21] and the inverted GNSS-like scheme denoted as
IGUULS (aka UL-TDoA) [13, 23, 24, 25, 26, 27]. However, BULS is commonly
referenced in the literature [23, 14, 18, 12, 13] as a conventional method,
often without providing detailed information on its specific implementation
and implications, assuming that readers are already familiar with the concept.
To the best of the authors’ knowledge, a comprehensive explanation of BULS,
from its theoretical foundations to practical use cases, is currently lacking
in the literature. Furthermore, BULS has not traditionally been considered a
strategic localization scheme, which has resulted in its unique properties and
potential applications being under-explored. Therefore, this article aims to
provide a thorough review of BULS as an elastic positioning topology for GNSS-
deprived zones without neglecting the importance of its counterparts. The
review covers the fundamental concepts, starting from the basics (Section 4.1
and 4.2), and extends to its practical applications (Section 4.3) using a
modular design principle approach. Moreover, we aim to make the content easily
understandable and accessible to a broad audience interested in UWB, requiring
minimal prior knowledge.

##  7 Concluding Remarks

UWB has been considered one of the most promising technologies for precise
positioning systems in GNSS-deprived indoor environments. Nowadays, miniature
UWB chips are distributed at relatively low prices in the electronic markets
by the leading manufacturers in the field including Decawave (i.e., now
Qorvo), NXP, Bespoon, Ubisense, etc. Recently, the UWB chip has been
integrated into the smartphones pioneered by Apple and followed by other
vendors. This signifies that the boom of UWB technology will soon be evident
in consumer electronic markets in our daily lives. Therefore, it is crucial to
address the UWB-based localization schemes from different aspects in order to
meet the diverse requirements of the applications in GNSS-deprived zones.

In this article, we comprehensively review the system model of an elastic
positioning scheme for UWB-based localization systems that we named
bidirectional UWB localization. In principle, the bidirectional UWB
localization scheme is one of the three design integration processes for a
time-based UWB positioning system as thoroughly discussed in Section 2.
However, the bidirectional UWB scheme was largely overlooked as a strategic
localization topology in the literature. In consequence, the potential of the
scheme for precise location-aware services in practice was excessively
overshadowed. The primary key benefit of the bidirectional UWB localization
scheme is that it can be used for both navigation and tracking tasks on demand
in the application. This gives the scheme a powerful ability to flexibly or
elastically combine two unique positioning perspectives (i.e., navigation and
tracking) within just a single scheme. In fact, the ability to combine the
mentioned two unique natures into a single scheme is a paradigm shift in the
way location-aware services are commonly observed. Consequently, the
bidirectional localization scheme enables many unique and new prospective
applications in the diverse fields of GNSS-deprived environments by banding
the navigation and tracking system as one common process.

Moreover, the article also highlights the prospect of the bidirectional scheme
in location services beyond UWB technology such as the positioning method for
the WiFi fine-time measurement, the 5G/6G wireless mobile network, etc.
Besides, the bidirectional localization scheme described in this article has
the potential to enable new applications, products, and systems in location-
aware services, which are unforeseeable and unaddressed in GNSS-deprived
indoor environments yet.

## Acknowledgment

This work was supported in part by the Cluster of Excellence Cognitive
Interaction Technology ‘CITEC’(EXC 277) at Bielefeld University, funded by the
German Research Foundation (DFG) and in part by the VEDLIoT project, which
received funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreement number 957197. The work of Cung
Lian Sang was also partly supported by the German Academic Exchange Service
(DAAD). The authors are responsible for the contents of this publication.

We also sincerely thank the anonymous reviewers for their valuable suggestions
and feedback to improve the manuscript.

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[]Cung Lian Sang received his B.E. and M.E. degrees in electronic engineering
from Mandalay Technological University (MTU), Mandalay, Myanmar in 2006 and
2008 respectively. In 2012, he obtained his M.Sc. degree in radar and radio
technology from National Research University, Moscow, Russian Federation. He
acquired his Dr.-Ing. degree in electrical engineering on precise indoor
localization systems using UWB technology from Bielefeld University,
Bielefeld, Germany, in 2022. He is currently a postdoctoral researcher at the
Congitronics and Sensor Systems group of CITEC. His research interests include
wireless communications, UWB, indoor localization, machine learning, and
tactile internet.

[]Michael Adams earned his B.Sc. and M.Sc. degrees in computer science from
Bielefeld University, Germany in 2012 and 2015, respectively.He acquired his
Ph.D. in computer science at the Cognitronics and Sensor Systems group within
CITEC in 2023. His primary research areas encompass machine vision and
wireless localization, displaying a keen interest in the interplay of
technology and spatial understanding. Additionally, Michael is deeply engaged
in machine learning, applying these principles to sports data science, where
he explores the potential for data-led decision-making and performance
prediction.

[]Marc Hesse (Member, IEEE), received his Diploma degree in electrical
engineering from the University of Paderborn, Germany, in 2010. He acquired
his Dr.-Ing. degree in electrical engineering in model-based design
methodology for wireless sensor nodes from Bielefeld University, Bielefeld,
Germany, in 2021. He is currently a PostDoc at the Cognitronics and Sensor
Systems group of the CITEC. His research interests lie in the systematic and
model-based development of embedded systems.

[]Ulrich Rückert (Member, IEEE), received the Diploma degree in computer
science and the Dr.-Ing. degree in electrical engineering from the University
of Dortmund, Germany, in 1984 and 1989, respectively. From 1985 to 1994 he
worked in the Faculty of Electrical Engineering, University of Dortmund, and
with the Technical University of Hamburg-Harburg, Germany. In 1995 he joined
as a full professor the Heinz Nixdorf Institute, University of Paderborn,
Germany, heading the research group System and Circuit Technology and working
on massive-parallel and resource-efficient systems. Since 2009 he is a
professor with Bielefeld University, Germany heading the Cognitronics and
Sensor Systems group of the CITEC. His main research interests include now
bio-inspired architectures for nanotechnologies and cognitive robotics.