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Review

UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review

by
Mohammed Faeik Ruzaij Al-Okby
1,2,
Steffen Junginger
3,*,
Thomas Roddelkopf
3 and
Kerstin Thurow
1
1
Center for Life Science Automation (Celisca), University of Rostock, 18119 Rostock, Germany
2
Technical Institute of Babylon, Al-Furat Al-Awsat Technical University (ATU), Kufa 54003, Iraq
3
Institute of Automation, University of Rostock, 18119 Rostock, Germany
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(23), 11005; https://doi.org/10.3390/app142311005
Submission received: 14 October 2024 / Revised: 18 November 2024 / Accepted: 24 November 2024 / Published: 26 November 2024
(This article belongs to the Special Issue Integrated Sensing and Communications: Latest Advances and Prospects)
Browse Figures
Figure 1
<p>UWB and other RF technologies’ frequencies and power spectral densities.</p> "> Figure 2
<p>Flowchart of the selection process.</p> "> Figure 3
<p>Triangulation IPS positioning technique.</p> "> Figure 4
<p>Trilateration IPS positioning technique.</p> "> Figure 5
<p>Multilateration positioning technique in a 3D environment.</p> "> Figure 6
<p>SDS-TWR tag–anchor signal propagation times.</p> "> Figure 7
<p>Topology of the TDoA positioning algorithm.</p> "> Figure 8
<p>PDoA positioning algorithm.</p> "> Figure 9
<p>Signal propagation path for UWB IPSs in (<b>a</b>) LoS and (<b>b</b>) NLoS.</p> "> Figure 10
<p>TWR IPS topologies: (<b>a</b>) anchor-based; (<b>b</b>) tag-based.</p> "> Figure 11
<p>TDoA IPS topologies: (<b>a</b>) TDoA; (<b>b</b>) RTDoA.</p> "> Figure 12
<p>IPS: (<b>a</b>) real-time testing environment; (<b>b</b>) 2D simulated environment with real-time object tracking using Python [<a href="#B80-applsci-14-11005" class="html-bibr">80</a>].</p> "> Figure 13
<p>Distribution of the used UWB modules.</p> "> Figure 14
<p>Distribution of the used UWB topologies.</p> "> Figure 15
<p>Distribution of the sensors and supporting technologies besides the UWB.</p> "> Figure 16
<p>Distribution of the used algorithms and filters.</p> "> Figure 17
<p>The main applications of the proposed references.</p> "> Figure 18
<p>The percentage range of errors for the included systems.</p> ">
Versions Notes

Abstract

:
Currently, the process of tracking moving objects and determining their indoor location is considered to be one of the most attractive applications that have begun to see widespread use, especially after the adoption of this technology in some smartphone applications. The great developments in electronics and communications systems have provided the basis for tracking and location systems inside buildings, so-called indoor positioning systems (IPSs). The ultra-wideband (UWB) technology is one of the important emerging solutions for IPSs. This radio communications technology provides important characteristics that distinguish it from other solutions, such as secure and robust communications, wide bandwidth, high data rate, and low transmission power. In this paper, we review the implementation of the most important real-time indoor positioning and tracking systems that use ultra-wideband technology for tracking and localizing moving objects. This paper reviews the newest in-market UWB modules and solutions, discussing several types of algorithms that are used by the real-time UWB-based systems to determine the location with high accuracy, along with a detailed comparison that saves the reader a lot of time and effort in choosing the appropriate UWB-module/method/algorithm for real-time implementation.

1. Introduction

Currently, the use of autonomous navigation devices and equipment is common in the industrial and scientific environment. The use of mobile robots is no longer limited to large factories and universities. Their use has become very widespread in all aspects of life, such as hospitals, shopping malls, airports, restaurants, and even homes. Autonomous navigation systems for mobile robots and moving devices need to provide constantly updated location information to optimally ensure accurate movement and a safe approach to the target destination. Furthermore, the movement of robots or self-driving equipment within these facilities may encounter some obstacles, leading to collisions that may result in damage of varying severity. Therefore, the need arises to use accurate tracking systems for their movement inside buildings and related facilities. These systems are commonly called indoor positioning systems (IPSs), real-time location systems (RTLSs), and real-time tracking systems (RTTSs).
Positioning can be defined as the process of using technology to monitor and determine the exact location of objects, robots, or people while stationary or moving. The first generations of positioning systems were able to efficiently track objects in outdoor environments based on the use of satellite technologies to provide Global Navigation Satellite System (GNSS) services such as GPS, GLONASS, Baidu, Galileo, and others.
The concept of indoor positioning systems, which represent the second type of positioning systems, has evolved with the development of devices and equipment operating with Internet of Things technologies. IoT solutions have provided the possibility of carrying out these complex tasks with high efficiency and reliability. The idea of developing indoor positioning systems emerged due to the inability of the Global Navigation Satellite System to track and determine the location of objects inside buildings. This results from the attenuation of signals sent and received, due to the walls of buildings, as well as the large margin of error of several meters. This makes the use of these techniques impractical due to low accuracy, as some applications inside buildings require high accuracy and an error rate that does not exceed several centimeters [1].
The attractiveness of the topic of developing indoor positioning systems has prompted many researchers and companies to develop different feasible solutions to achieve this goal. The proposed solutions rely on the use of available IoT-based technologies such as Bluetooth Low Energy [2], Wi-Fi [3], ultra-wideband technology [4], vision-based techniques [5], acoustic waves [6], radio frequency identification (RFID) [7], and others. Choosing the appropriate IPS for determining object locations in indoor environments depends primarily on the nature of the place to be covered, in terms of area, and the presence of obstacles such as walls and furniture. The nature of an open space such as a warehouse or stadium differs from that of inhabited buildings such as laboratories and residences, which contain narrow corridors and large amounts of furniture (obstacles). The choice also depends on the type of application, the required accuracy, the speed of updating data, and the cost allocated for implementation.
In this work, a deep and focused search was conducted in the literature to review research articles, conference papers, book chapters, and studies carried out practically for finding the positioning and tracking the motion of objects using ultra-wideband technology, as this is considered to be one of the best solutions currently available. Unlike other radio frequency waves (RF), Wi-Fi, and Bluetooth, the ultra-wideband waves are characterized by enabling accurate measurement of the time of flight between its units and, thus, the possibility of accurate measurement of distance, direction, and location determination with a relatively small margin of error compared to other RF waves. Ultra-wideband waves have a much larger bandwidth than narrowband waves. This results in ultra-wideband waves having a very short wavelength, enabling the temporal accuracy to be very high, which is reflected in high accuracy in measuring time of flight and also contributes to improving the resistance to multipath interferences and fading. Furthermore, the large bandwidth and low propagation time make the use of ultra-wideband waves ideal for real-time systems, especially those that require a quicker response time, as they are about 50 times faster than GPS systems and hundreds of times faster than traditional Bluetooth systems. These characteristics make ultra-wideband the ideal choice in automation applications, as well as applications that deal with fast movement of objects to be tracked and high-speed applications [8]. Due to the large frequency range where UWB transmissions are allowed (3.1–10.6 GHz), the transmission power was limited to −41.3 dBm to avoid and reduce interference with waves of other technologies operating within the same frequency range. Figure 1 summarizes the main characteristics of UWB and other RF technology frequencies and power spectral densities.
Several keywords were used to import the related research works—such as “UWB, Positioning, Localization, Tracking, IPS, RTLS, RTTS”—from several databases, including IEEE, Elsevier (Scopus and ScienceDirect), Clarivate Web of Science (WoS), PubMed, and the ACM Library, during the past five years between January 2019 and June 2024. More than 270 research papers were retrieved from the aforementioned sources to examine their suitability for inclusion in this study. The inclusion/exclusion criteria were based on several factors related to the nature of the system used and the modernity of the work. The importance of the present work lies in providing a comprehensive view of indoor positioning systems operating with ultra-wideband technology in real time, and enabling researchers and engineers interested in this type of system to identify the available commercial units and their technical specifications, as well as the methods, techniques, and algorithms that enable the reader to reduce the effort and time expended in developing the relevant systems.

2. Materials and Methods

In this work, the focus was on real-time indoor positioning systems based on ultra-wideband technology, because of the important advantages that they offer, which make them one of the most effective, robust, and practical solutions for indoor positioning, localization, and tracking tasks for mobile objects. Furthermore, this paper concentrates on the available UWB chips, modules, and solutions that can be used in the real-time implementation of object tracking, positioning, and localization applications.

2.1. Search Strategy

The present work includes the available research references for the past 5 years, from January 2019 until August 2024. Several keywords were used to import the most relevant articles, conference papers, book chapters, and studies, such as object tracking, localization, positioning, indoor object tracking, indoor localization, indoor positioning, indoor positioning system, real-time tracking system, real-time localization system, ultra-wideband, UWB tracking, multi-tag positioning, time of arrival (ToA), time of departure (ToD), time difference of arrival (TDoA), angle of arrival (AoA), phase difference of arrival (PDoA), two-way ranging (TWR), multilateration, trilateration, and triangulation. The search was initially started on the IEEE Explore and ScienceDirect databases and then extended to the other listed databases. More than 600 related papers were imported from IEEE, Elsevier (Scopus and ScienceDirect), WoS, PubMed, and the ACM Library for the mentioned period for eligibility. A group of the most relevant papers was selected to be discussed in the present work. We focused on several points that helped with the inclusion and exclusion criteria. The systematic review was carried out following the PRISMA protocol.

2.2. Inclusion and Exclusion Criteria

The term “real-time” refers to the localization, tracking, and positioning systems that have been practically implemented, tested, and can be used in real-time implementation. Furthermore, limited the inclusion of the selected research to systems for indoor environment applications. Since many technologies can be used to build indoor positioning systems, the focus was on scientific research and systems that operate with ultra-wideband technology, especially those used for mobile object tracking applications such as mobile robots and unmanned aerial vehicles (UAVs). Accordingly, the inclusion and exclusion criteria were as follows:
  • Inclusion criteria
    -
    Real-time systems, positioning algorithms, and filters for a real-time system.
    -
    Systems for indoor application.
    -
    Can be used for mobile object tracking (robots, UAVs, etc.).
    -
    Complete IPS/RTLS system including hardware, software, and monitoring server (object coordinates, tracking, and positioning can be monitored).
  • Exclusion criteria
    -
    Simulation, theoretical research, and algorithms; UWB manufacturing research.
    -
    Systems for outdoor environments.
    -
    Systems cannot be used for mobile object applications.
    -
    Partial work (incomplete system; cannot implement real-time tracking and monitoring; work for solving a partial issue).
    -
    Works that do not include any information about the UWB components used.
Based on the inclusion criteria, 147 papers were included that met the aims of the proposed study. Figure 2 illustrates the flowchart of the system selection process.

3. UWB Positioning System

Ultra-wideband systems have emerged as a promising technology in the field of position monitoring, especially for moving objects within a closed indoor environment. The quality of the pulses issued by ultra-wideband transmitters is characterized by high temporal accuracy, which makes it possible to measure the transmission and reception time (time of flight) easily, enabling high precision in determining the location. The accuracy can vary from a few decimeters to centimeters in optimal conditions [9]. Most position detection systems usually consist of two main components, which may be identical in terms of design and physical components but different in terms of the method of use within the system: the anchor, and the tag.
In most of the current systems, anchors are used as receivers for the signal issued by the tag, and to determine the distance between the anchor and tags and then forward the measurement to the IPS controller. Numerous anchors are used, depending on the area of the place to be covered. Currently, the coverage of a single anchor operating with ultra-wideband technology has reached approximately 100 m in line-of-sight conditions (LoS: the state of having a direct connection between the sender and the receiver without any obstacles). This contributes to reducing the cost of the infrastructure used, which represents one of the factors that limit the use of these systems. Anchors are installed in a specific way and with dimensions that depend on the type of method or algorithm used to determine the position.

3.1. UWB Positioning Techniques and Algorithms

Several techniques can be used with ultra-wideband waves to determine the indoor position of movable objects. In general, the UWB location tracking of the unknown movable tag can be determined using one of the following basic techniques: triangulation, or trilateration [10]. These techniques have been derived from ancient mathematical calculations to determine the locations of things.

3.1.1. Triangulation Technique

The triangulation technique is based on calculating angles. The location of a moving tag can be identified if the location of two fixed anchors is known, by estimating the angles between the movable tag and the antennas of the fixed anchors and intersecting the extension of the moving tag with the known line between the two fixed anchors, which enables the accurate determination of the location of the moving tag (see Figure 3).
In Figure 3, the coordinates of anchor1 and anchor2 are ( x 1 ,   y 1 ) and ( x 2 ,   y 2 ), respectively, and the coordinates of the movable tag are ( x t ,   y t ). Two angles were observed between the anchors, the movable tag, and the west–east line:
θ1 = A1 T E (the angle between anchor1 (A1), tag (T), and E horizontal line);
θ2 = A2 T E (the angle between anchor2 (A2), tag (T), and E horizontal line).
The slope of the line A1 T can be calculated using the following formula:
t a n θ 1 = y t − y 1 x t − x 1
We can repeat the same procedure for the line A2 T:
t a n θ 2 = y t − y 2 x t − x 2
By solving Equations (1) and (2), we get
x t = y 1 − y 2 + x 2 t a n θ 2 − x 1 t a n θ 1 t a n θ 2 − t a n θ 1
and
y t = y 1 t a n θ 2 − y 2 t a n θ 1 − ( x 1 − x 2 ) t a n θ 2 t a n θ 1 t a n θ 2 − t a n θ 1

3.1.2. Trilateration Technique

The trilateration technique depends on calculating the distances between the moving tag and the fixed anchors. The distance is calculated by multiplying the time of wave propagation (ToF) by the speed of the UWB wave, which is approximately (3 × 108 m/s). If the coordinates of the used anchors (which are predefined by the user) and the distances between the movable objects and anchors can be provided by the used technology, then we can find and track the object location using trilateration techniques.
The distance d between any two points can be calculated with the known coordinates (x, y) of the two points; for example, the distance between (x1, y1) and (x2, y2) is
d = ( x 2 − x 1 ) 2 + ( y 2 − y 1 ) 2
If the tag coordinates are ( x t ,   y t ) and the anchor coordinates are ( x a ,   y a ), we can rewrite the equation as follows:
d a 2 = ( x a − x t ) 2 + ( y a − y t ) 2
d a 2 = x a 2 + x t 2 − 2 x a x t + y a 2 + y t 2 − 2 y a y t
when a point (r) is considered as a reference point for the IPS and used to fix an anchor with the coordinates ( x r ,   y r ), and the remaining anchors have the coordinates ( x i ,   y i ), where i represents the anchor number. Subtracting the dr from di results i:
d r 2 − d i   2 + x i 2 + y i 2 − x r 2 − y r 2 = 2 ( x i − x r ) x t + 2 ( y i − y r ) y t
With r = 1 and a = 2,3, … ((i) represents all of the used anchors), we obtain
2 ( x 2 − x 1 ) 2 ( y 2 − y 1 ) 2 ( x 3 − x 1 ) 2 ( y 3 − y 1 ) x t y t = d 1 2 − d 2   2 + x 2 2 + y 2 2 − x 1 2 − y 1 2 d 1 2 − d 3   2 + x 3 2 + y 3 2 − x 1 2 − y 1 2
By representing Equation (8) by the linear system equation (Ax = b), x will be the coordinate of the movable tag ( x t ,   y t ), which can be processed and monitored using a proper positioning algorithm and monitoring program. Figure 4 explains the structure of the trilateration technique for a three-anchor IPS.
The great industrial development and the effective introduction of robots into production processes has coincided with the need for three-dimensional (3D) positioning systems. These systems differ from the common two-dimensional systems in the need to calculate the height (h) of the moving tag in addition to the (x, y) coordinates (see Figure 5). Some 3D positioning systems require an additional anchor fixed at a different height than the rest of the IPS anchors to measure the height of the moving tags. From Equation (8), if we suppose that the number of the anchors used in the system is (i), we can calculate the 3D position coordinates of the movable tag ( x t ,   y t ,   h t ) as follows:
2 x 2 − x 1 2 y 2 − y 1 2 h 2 − h 1 2 x 3 − x 1 2 y 3 − y 1 2 h 3 − h 1 ⋮ ⋮ ⋮ 2 x i − x 1 2 y i − y 1 2 h i − h 1 x t y t h t = d 1 2 − d 2   2 + x 2 2 + y 2 2 + h 2 2 − x 1 2 − y 1 2 − h 1 2 d 1 2 − d 3   2 + x 3 2 + y 3 2 + h 3 2 − x 1 2 − y 1 2 − h 1 2 ⋮ d 1 2 − d i   2 + x i 2 + y i 2 + h i 2 − x 1 2 − y 1 2 − h 1 2

3.2. UWB IPS Topologies

The following subsections discuss in detail selected main topologies used for UWB IPSs.

3.2.1. Two-Way Ranging (TWR)

Two-way ranging is a basic and effective method for calculating indoor objects’ position. The distance between an anchor and a tag is calculated by measuring the time of flight (ToF) of ultra-wideband waves between them, where the time of flight of the waves between the sender and the recipient is multiplied by the speed of light (the speed of propagation of radio waves). There are several methods to implement TWR, including single-sided TWR (SS-TWR), symmetrical double-sided TWR (SDS-TWR), alternative double-sided TWR (AltDS-TWR), and asymmetrical double-sided TWR (ADS-TWR). In a two-coordinate medium, at least three anchors are required to calculate the positioning of the tag, where each tag–anchor distance is calculated separately and represents the radius of a tag–anchor circuit. The tag position can be accurately calculated by intersecting the three tag–anchor circles, where the intersecting point represents the object’s expected position [11,12,13]. Figure 6 explains the UWB signal propagation through the distance calculation phase in SDS-TWR. To measure the distance between an anchor and a tag, three messages need to be exchanged. The tag starts the TWR by sending the first poll message request to the selected anchor, which takes the time of flight t1 to reach the anchor. The anchor requires the time Td1 (delay time) to record and process the request and replay the response to the tag. The total time from the tag request until the tag receives the anchor response is the round time Tr1 = t1 + Td1 + t2 = 2ToF + Td1. The tag records the time Tr1 and composes and sends the final set message, including the final data packet, which includes all of the required time data to calculate the distance by the anchor. The second-round time Tr2 starts from the anchor response until the anchor receives the final packet Tr2 = t2 + Td2 + t3 = 2ToF + Td2. From Figure 6, we can calculate the UWB signal’s ToF using the following equation [14]:
T o F = ( T r 1 + T r 2 − T d 1 + T d 2 ) 4

3.2.2. Time Difference of Arrival (TDoA)

This algorithm determines the position by measuring the difference in the arrival time of the signal sent from the tag to the different anchors. The difference in distance from each anchor is calculated by the difference in arrival time at each anchor. This results in multiple hyperbolas passing through the tag’s location. The intersection of these hyperbolas is the tag’s location. One of the advantages of this method is that there is no need to send a response message from the anchors to the tag, as the arrival time of the signal at each anchor is calculated separately, and then the difference between the arrival times is subtracted to determine the location of the moving tag. This saves processing time and increases the speed of the system. This method requires synchronization between anchors, which adds complexity to the implementation of this algorithm. However, this algorithm has great flexibility in the number of tags that can be tracked at the same time. Synchronization can be implemented using a direct wired connection between the anchors, which is more accurate. Synchronization can also be achieved wirelessly, and in this case, synchronization requires periodic calibration to obtain the required accuracy [15,16,17,18]. Figure 7 explains how the algorithm works. The tag begins sending a request to the neighboring anchors, depending on their location on the tag. After the signal reaches all three anchors, it takes different times to arrive at t1, t2, and t3. By calculating the difference between the three times, the location of the tag can be determined.
This method can be used differently, where the poll UWB signals are sent by the synchronized anchors, the different times of arrival for each anchor are calculated separately in the mobile tag, and the location is determined based on the difference between the different times of arrival for the anchors. This method is called reverse time difference of arrival (RTDoA).

3.2.3. Phase Difference of Arrival (PDoA)

This method is based on measuring the difference in phase between the ultra-wideband waves received from the transmitter (tag or anchor). Using this algorithm, the location can be found with the smallest possible number of anchors and tags by integrating two or more antennas (antenna array) to receive the signal in the receiver. This algorithm is used to estimate the angle of arrival or departure (AoA or AoD), as well as the distance between the sender and the receiver (ToA or ToD). In the previous methods, UWB units containing one antenna were used to calculate the time of flight and convert it into distance. In this method, a location can be found between a tag and an anchor, one of which contains two or more antennas with a distance d between them, without the need for additional system infrastructure. The recipient measures the phase difference between the signal received on each of the two antennas, and from this information, the algorithm can find the angle of the received UWB waves and determine the values of θ1 and θ2 as well as the distance between the sender and each antenna separately by calculating the time-of-flight values t1 and t2, and this enables the location to be determined without the need for additional anchors. This algorithm is widely used for angle-of-arrival or angle-of-departure (AoA or AoD) estimation in IPSs. Figure 8 explains the structure of the PDoA algorithm [19,20,21,22].

3.3. Optimization Filters and Fusion-Based Algorithms for IPSs

Since the emergence of indoor positioning systems, and due to the margin of error in determining the correct location, important attempts have been made to reduce the error resulting from the delay time in the antennas or from the passage of waves through obstacles. The antenna delay time occurs when signals are exchanged between ultra-wideband units; a time delay occurs in transmission and reception as the signal propagating through its analog circuits suffers from delays in the transmitting and receiving antennas. Such delays can lead to range estimation errors between system units and, ultimately, affect the accuracy of real-time positioning systems unless these errors are measured and corrected [23]. Moreover, the delay resulting from the passage of signals through obstacles leads to an error in the accuracy of the system’s performance. This causes incorrect measurements of the wave propagation time (ToF), through which the distances between the system units are calculated to determine the objects’ location. Many methods, algorithms, and filters have been used to reduce this type of error. In the following sections, the most important of them are briefly explained.

3.3.1. Least Squares Algorithm (LS)

The least squares method is used to reduce the margin of error in indoor positioning systems by finding the closest path between the coordinates of the tag movement to be tracked. After obtaining the coordinates of the tag movement (xt, yt) from Equation (8) and the linear equation (Ax = b), the estimated coordinates can be calculated using the least squares method as follows:
A = 2 ( x 2 − x 1 ) 2 ( y 2 − y 1 ) 2 ( x 3 − x 1 ) 2 ( y 3 − y 1 )
x = x t y t
b = d 1 2 − d 2   2 + x 2 2 + y 2 2 − x 1 2 − y 1 2 d 1 2 − d 3   2 + x 3 2 + y 3 2 − x 1 2 − y 1 2
x = ( A T A ) − 1 A T b
where A is an m × n matrix, x is an n × p matrix, and b is an m × p matrix. The “[]T” in Equation (11) represents the matrix transpose. Using Equation (11), the coordinates of the location of the guessed tag can be calculated depending on the provided coordinates of the anchors’ location using the least squares method.

3.3.2. Kalman Filters (KFs)

The Kalman filter is a recursive estimation method that is typically used to calculate or predict the states of a system based on a model or noisy measurements of it considering additive white Gaussian noise. Its method of operation is based on predicting the next state based on information from the previous state. It calculates the values of the states of a dynamic system in an optimal way that minimizes the expected value of the square of the difference between the prediction and the correct state, and then uses them as inputs for the next iteration [24]. Many developments have been made to the regular Kalman filter to improve its operation, resulting in new types of this filter, the most prominent of which are the extended Kalman filter (EKF) and the unscented Kalman filter (UKF).
The limitations of the Kalman filter in dealing with primarily linear systems led to the development of the extended Kalman filter, which represents the nonlinear version of the Kalman filter. The unscented Kalman filter (UKF) was proposed by Goller and Ullmann in 1997. The central operation of the Kalman filter is to propagate a Gaussian variable (GV) through the dynamics of the system. In the EKF, the state distribution is approximated by the GV, which is then propagated through a first-order linear equation of the nonlinear system. This process can introduce large errors in the posterior mean and variance of the transformed GV, which can result in poor performance and deviations from the true value. The UKF is used to address this problem, using deterministic sampling. The state distribution is again approximated by the GV, which is now represented by a set of carefully selected sample points rather than the single average point in the EKF. The captured sample points contain the true mean and variance of the GV. When propagated through the true nonlinear system, the posterior mean and variance are accurately scaled to the third degree (Taylor series expansion) of the nonlinear state. This is not possible with the EKF, as it does not exceed first-class accuracy [25,26].

3.3.3. Particle Filter (PF)

Particle filter (PF) is another application of Bayesian filters that is used for position estimation or to solve state estimation problems. PF generates particles to represent the state of the system, where each particle represents a particular state of the position and direction of the tracked object (tag). When the object to be tracked starts moving, the filter generates particles with a normal distribution, where there is no prior information about the location of the tag. The coordinates and orientations of the tag are generated and distributed according to the geometry of the location. This process is called sampling, where each particle has its own weight parameter (number). This number represents the probability of the system being in a particular location or state, and the larger the number (weight parameter), the greater the probability of finding the tag in the relevant location. Then, as the tag continues to move, the measurement data captured by the sensors provide more accurate information about the location of the tag being tracked [27,28].

4. Commercial UWB Modules and Chips for IPSs

Many companies that produce and market real-time location and indoor positioning systems use off-the-shelf ultra-wideband modules to reduce the time, cost, and complexity of manufacturing these modules themselves. In this section, we will discuss the most popular commercial ready-made units that are used in indoor positioning systems.

4.1. Qorvo DW1000 UWB Family

The Qorvo (Previously DecaWave) DW1000 (Qorvo Inc., Greensboro, NC, USA) is one of the first ultra-wideband units that contains all basic circuits for the transmission and reception process in one chip. It began to spread in the middle of the last decade and is still widely used today, despite the emergence of modern competing products. This product provides different attractive features that enable its widespread use, such as low cost, measurement accuracy of up to 10 cm in line-of-sight conditions, low power consumption compared to cellular radio modules, working on six channels within the radio frequencies 3.5 to 6.5 GHz, data transfer capacity of up to 6.8 Mbps, and the ability to communicate over distances of more than 200 m in the case of line-of-sight (LoS) communication. It can be used for both two-way ranging and TDoA algorithms for IPSs and can be hosted by any processor with an SPI interface [29,30,31].

4.2. Qorvo DW3000 UWB Family

The DecaWave DW3000 transceiver module (Qorvo Inc., Greensboro, NC, USA) is the advanced version of the previous family DW1000 [32]. The module is an IEEE 802.15.4-2011 [33] and IEEE 802.15.4z [34] UWB module based on the Qorvo DW3000 chips, which include four different versions (3110, 3120, 3210, and 3220) depending on the package and the AoA/PDoA measurement capability. It is a fully integrated UWB transceiver with a ceramic antenna, clock circuit, power management, and all required RF circuits in one module. The module has high multipath fading immunity and can be used in two-way ranging (TWR) or time difference of arrival (TDoA) location algorithms to execute real-time tracking and positioning tasks. It supports channels 5 and 9 (6489.6 MHz and 7987.2 MHz) and data rates of 850 kbps and 6.8 Mbps. This module can be integrated with any host processor for IPS implementation and supports SPI interfaces of up to 38 MHz. It is interoperable with the Apple U1 and U2 chips and supports high tag densities in real-time location systems.

4.3. NXP Trimension UWB Chips Family

The Trimension UWB family (NXP Semiconductors, Eindhoven, The Netherlands) has many products that are well customized to suit different applications. Trimension SR150 is used for UWB tags and anchors in IoT applications, and it is ideal for applications with large-scale infrastructure. It has a pre-installed FiRa™ stack (which stands for “fine ranging”), which has the integrated FiRa™ MAC, supporting interoperability with a set of UWB devices in the market. It adds angle-of-arrival (AoA) technology for high-precision applications, and it is interoperable with IEEE 802.15.4z higher-pulse-repetition UWB. Other members of the Trimension family of UWB chips, such as SR150, SR040, OL23D0, NCJ29D5, and NCJ29D6, can be used with different technologies and applications [35,36].

4.4. Microchip ATA8350/ATA8352 UWB Transceivers

The Microchip ATA8350 UWB transceiver (Microchip Technology Inc., Chandler, AZ, USA) is designed for measuring hardware-controlled sequential distance for localization. It is compliant with UWB regulations and supports a frequency range of 6.2–7.8 GHz with a data rate of 246 Kbps. It can be hosted by any processor with an SPI interface of up to 20 MHz and can measure the ToF with a resolution of ±15 cm. The ATA8352 transceiver has a wider frequency range of 6.2–8.3 GHz, transfers higher data rates of up to 1 Mbps, has SPI control with up to 24 MHz, and supports the time difference of arrival (TDoA) approach. It has a more accurate ToF measurement resolution of ±4.5 cm, which reflects better system accuracy [37].

4.5. Infineon 3DB6830 UWB IC

The Infineon 3DB6830 (Infineon Technologies Switzerland AG, Zurich, Switzerland) is a low-power IEEE 802.15.4f [38]-compatible UWB transceiver with an integrated security layer that makes it more suitable for distance measurement for security applications. The IC measures the distance based on direct ToF measurement and can be driven by any host processor or microcontroller that can execute random number generation and authentication routines. It works within the upper UWB bands from 6 to 8 GHz. It can cover up to 120 m in LoS communication conditions, with ±10 cm accuracy [39].

4.6. SPARK SR1010/SR1020 UWB Transceiver

The Spark SR1010/SR1020 (Spark Microsystems International Inc., Montreal, QC, Canada) is a low-power UWB transceiver suitable for energy-efficient communications with ultra-short latency and supports a reconfigurable frequency range of 3.1–6 GHz for SR1010 and 6–9.3 GHz for SR1020. The transceiver’s data rate can be contentiously scaled from 1 Kbps to 10 Mbps to achieve ultra-low power consumption over a wide range of data rates, which makes it a flexible solution for many applications. These chips can measure the distance by measuring the time of flight of the generated UWB signal between two chips, with a range approaching 100 m in LoS conditions, and with a measurement accuracy of about ±30 cm [40].

4.7. B-UWB-MOD1

The B-UWB-MOD1 (STMicroelectronics, Geneva, Switzerland) is an ultra-wideband module designed for implementing high-precision indoor positioning and location systems. It is an ultra-compact surface-mounted module that is designed to work with UWB frequencies from 3.25 to 4.75 GHz and support the UWB channels 1, 2, 3, and 4 with bandwidths of 500 MHz and 1 GHz. It can cover ranging distances of up to 600 m in LoS conditions with 10 cm distance measurement accuracy and can provide an adjustable sample rate of up to 250 per second. The data can be acquired using the UART communication bus. This module is currently used as the core element of the commercial evaluation kit B-UWB-MEK1 (STMicroelectronics, Geneva, Switzerland), which is used for the prototyping, evaluation, and demonstration of indoor location systems [41].

4.8. Summary of the Commercial UWB Modules and Chips for IPSs

In this section, we present a summary of the most prominent characteristics of some available commercial chips and units available on the market that can be used in the design and development of real-time indoor positioning systems operating with ultra-wideband technology, as shown in Table 1.

4.9. Commercial UWB-Based IPSs

The growing need and demand for indoor positioning systems has led many companies, vendors, and factories to focus on manufacturing and developing commercial systems that suit market requirements. A complete package is developed that includes the hardware components and compatible software enabling the system to display the tasks required to be performed optimally. Table 2 reviews selected providers of indoor positioning systems operating with ultra-wideband technology.

5. Real-Time UWB Indoor Positioning Systems (RTIPSs)

The urgent need to develop real-time indoor positioning systems has emerged with the great developments in the sciences of automation engineering and robotics in particular, and in the sciences of electrical engineering and informatics in general, to provide the possibility of accurate tracking of self-driving systems such as robots and drones in closed environments, i.e., inside buildings. The development of real-time tracking and indoor positioning systems faces many challenges that affect the performance of the systems and limit the required accuracy.

5.1. Obstacles and Non-Line-of-Sight Obstructions

Calculating the wave propagation time with high accuracy leads to determining the location with high accuracy, but the time calculation may be subject to errors that cause major defects in the system’s performance in general; this is especially the case inside buildings, where many sources of interference and obstacles cause defects in the calculation of the wave propagation time. An error in calculating the real distance leads to an error in determining the real location of the object to be found or tracked.
Fading is one of the common phenomena that affect the performance of ultra-wideband communication systems. It occurs when the signal propagates in multiple paths, which causes signal fluctuation that affects the strength and quality of the UWB signal, leading to errors in time-of-flight calculation and increasing the system error rate.
Shadowing is another common and more popular phenomenon that affects the performance of IPSs. It occurs when the UWB signals are attenuated or blocked by obstacles in the indoor environment [67]. The fading and shadowing effects are critical parameters to be considered during the system performance and error analysis as part of the UWB-based indoor positioning wireless communication system [68]. One of the most important problems affecting the accuracy of measuring propagation time is the presence of obstacles in the path of direct wave propagation between two points, which is called the non-line-of-sight (NLoS) problem. In this case, there are two possibilities: The first is for the wave to penetrate the obstacle and complete its path to the target point. In this case, the error in measuring the distance occurs due to the delay resulting from adding the penetration time. This increases the time taken to reach the target, which leads to an increase in the calculated distance and, thus, an error in determining the location. In the second possibility, the direct wave is unable to penetrate the obstacle; thus, it is reflected or deflected. In this case, the reflected or deflected indirect waves may reach the target, traveling a greater distance by bouncing off walls or obstacles, which adds a delay time that may be greater than in the first case, resulting in a greater error in determining the location of the target to be tracked [69,70]. Figure 9 explains the NLoS problem in UWB-based IPSs.
Many researchers have suggested different solutions and methods to address errors caused by the non-line-of-sight problem. Yang et al. [71] proposed an efficient cooperative positioning algorithm to mitigate the environmental noise and enhance the performance and accuracy of IPSs in non-line-of-sight environments. The algorithm is based on selective tags’ coordinate information sharing, and it includes three steps: initial positioning, tags classification, and precise positioning. The evaluation process is conducted in a non-line-of-sight environment, and the evaluation results show a 71.7% accuracy improvement. He et al. [72] proposed an approach that combines Discrete Wavelet Transform (DWT) and convolutional neural networks (CNNs) to solve non-line-of-sight ultra-wideband signal recognition. The UWB signal coefficients are extracted from the Channel Impulse Response (CIR) using DWT, and then the coefficients are fed as inputs to a CNN to explore deep features to distinguish between NLoS and LoS signals. The experimental results revealed that this method achieves better accuracy compared to other approaches. Qin et al. [73] proposed a technique for compensating the NLoS occlusion errors of the UWB IPS signals using a micro-electromechanical system (MEMS) inertial measurement unit (MIMU)-assisted UWB localization. The NLoS occlusions of UWB devices are identified with the channel information of the UWB transceivers and tag–anchor distance variation patterns. The localization process is implemented by fusing the UWB and IMU data with an unscented Kalman filter (UKF) framework. The data fusion of the system is adjusted according to the NLoS effect on the UWB signals. System tests have shown high efficiency in identifying and compensating for errors caused by NLoS in the used UWB IPS modules. Su et al. [74] proposed a system (UWBLoc) to mitigate the NLoS errors in IPSs using ranging residuals to characterize the influence of the environment on ranging and measuring distance and angles using the Taylor-series-based least squares (TS-LS) method. Similar approaches have been proposed in [75,76,77,78].

5.2. Real-Time Implementation Ranging and Topologies

The unique properties of ultra-wideband waves have supported their widespread use in real-time positioning systems. As explained in the previous paragraphs, several companies have introduced products that support the direct development of tracking and positioning systems using different transceivers and modules based on ultra-wideband technology. Below are the most prominent research works focusing on indoor tracking and positioning systems using ultra-wideband technology and completed during the survey period described above, which represents the main contribution of this work.

5.2.1. Two-Way Ranging-Based Indoor Positioning Systems

This method is one of the common methods for implementing real-time tracking and positioning systems. It can be implemented using two topologies: The first uses a reference anchor to calculate the distances between the ultra-wideband units and send them to the monitoring and display server via a cable or wireless connection. The second approach calculates the distances between the moving tag and the active anchors in the moving tag itself and sends the data via the cloud to the monitoring and display server. Figure 10 shows these two topologies [8].
Many tracking and positioning systems have been implemented using this method. He et al. [79] proposed a single-sided two-way ranging (SS-TWR) positioning system based on filtering and clock compensation. The system uses the Qorvo DW1000 (Qorvo Inc., Greensboro, NC, USA) UWB modules for both tags and anchors. The proposed system uses the anchor-based topology and consisted of three anchors and one tag. The reference anchor is connected by wire to the server laptop using a serial port. The random forest algorithm estimates new distances based on previous SS-TWR algorithm values. The results of both random forest and SS-TWR algorithms have been evaluated using the root-mean-square error (RMSE), and the results showed better performance for the random forest algorithm, with an improvement of approximately 4.85 cm compared to SS-TWR. Al-Okby et al. [80] proposed a low-cost multi-tag IPS for object tracking in indoor environments. The system’s UWB hardware uses the MaUWB_DW3000 (Makerfabs Corporation, Shenzhen, China) module, which includes the new Qorvo DW3000 UWB transceiver. The system consists of three fixed anchors and can track up to 16 movable tags. The trilateration technique with the least squares method has been used for system implementation and testing in both LoS and NLoS situations. All UWB measurements from the system’s UWB units are accumulated by the reference anchor and forwarded to the control and monitoring server laptop via a COM port. A Python-3.7 program is used for monitoring the movable tag based on the map of the testing area. The practical tests indicate that the system can achieve an accuracy of 50 cm. Cheng et al. [81] proposed the design of an IPS based on TWR and trilateration algorithms. The system consists of four anchors and one movable tag. All of the units have similar hardware designs, including the Qorvo DW1000 UWB modules for distance measurements and the STM32F103ZET6 (STMicroelectronics, Geneva, Switzerland) microcontroller for data preprocessing and UWB control tasks via an SPI communication bus and powered by a lithium battery. The experimental tests proved a low error rate of approximately 14 cm, and the authors suggested this system for underground coal mine personnel. Le Minh et al. [82] proposed a double-sided two-way ranging (DS-TWR)-based positioning system with a Kalman filter for improving the system accuracy. The system used the Qorvo DW1000 UWB modules for system implementation, with four fixed anchors and one movable tag responsible for the distance calculation with all ultra-wideband units and communication with the monitoring station (tag-based topology). The use of the Kalman filter enhances the system’s performance and decreases the distance measurement errors in both LoS and NLoS environments. Qi et al. [83] used a derivative unscented Kalman filter (DUKF) and root-mean-square error (RMSE) to estimate and reduce the nonlinear error of anchor positions in a TWR-based IPS. The system used the Qorvo DW1000 UWB modules and micro-electromechanical system (MEMS) inertial sensor for real-time implementation with the anchor positions’ error calibration, which improved the localization precision and robustness of the system. Sidorenko et al. [84] investigated the multiple error corrections of clock drift between the reference anchor and the tag for the DecaWave DW1000 transceiver (Qorvo Inc., Greensboro, NC, USA) and analyzed the most common TWR protocols in real-time implementations. The test results showed that alternative double-sided two-way ranging (AltDS-TWR) has the best results, and that it can correct the signal power and hardware delays for every UWB unit individually. The used UWB unit was the Qorvo (DecaWave) EVK1000, which consists of the DW1000 IC with the ST32F105 (STMicroelectronics, Geneva, Switzerland) microcontroller and has a built-in LCD, various interfaces (JTAG, SPI, USB), and a wideband planar omnidirectional antenna. Laadung et al. [85] proposed two general active–passive two-way ranging (TWR) methods for UWB-based IPSs: The first (AP1-TWR) uses the UWB packet exchange and listens to the other anchors’ transmissions, with knowledge about the anchors’ locations. The second (AP2-TWR) only listens to the other anchors’ transmissions. The experimental tests were conducted using the Eliko UWB RTLS (OÜ Eliko Technology, Tallinn, Estonia), which is based on the DecaWave DW1000 module. Five fixed anchors and one tag were used, and the distances between the anchors and the anchor coordinates were measured using the DISTO S910 (Leica Geosystems AG, Heerbrugg, Switzerland) laser distance meter. Both methods were tested with the SS-TWR, SDS-TWR, and AltDS-TWR active ranging methods. The results indicated that the AP2-TWR method consistently outperforms the AP1-TWR method by about 10 to 20%. Herbruggen et al. [86] proposed a real-time approach for optimal anchor node selection in TWR. Instead of ranging with all nearby anchors, including those with poor channel characteristics, the new method allowed for the selection of the best-performing anchors based on the best link quality. This new approach enhances the accuracy by up to 15 cm, which represents a 50% improvement compared with traditional TWR. The practical tests were implemented using the Wi-Pos UWB open-source platform, which includes the Qorvo DW1000 transceiver with wireless backbone. Other TWR-based IPSs have been proposed in [87,88,89,90,91,92,93,94,95,96,97].

5.2.2. Time Difference of Arrival (TDoA)-Based Indoor Positioning Systems

This is a flexible and scalable method for determining the location of moving tags by recording the difference in the arrival time of the signal between active anchors that must be synchronized. The advantage of this method is its low power consumption, because the identification wave needs to be sent only once from the tag to the anchors, without the need for a response or confirmation wave. The timestamps recorded at all anchors are collected by the reference anchor or a gateway and forwarded to the tracking monitoring server, where the location coordinates are calculated and the desired movement of the tags is tracked.
It is also possible to implement this method in reverse, called reverse time difference of arrival (RTDoA). The anchors send synchronized blinks at specific time intervals in the active tracking area. When the moving tag enters the specified area, it receives the blinks and calculates the distances between it and the anchors using TDoA. All of the calculated distances are then forwarded to the monitoring server for implementing real-time tag monitoring using multilateration. Figure 11 shows the two TDoA topologies [8].
This method has been widely used in the implementation and development of indoor tracking and positioning systems due to its unique advantage of saving energy consumption, especially since the targeted systems use batteries extensively in their operation. Many studies, system developments, system tests, and evaluations for TDoA have been proposed. Shyam et al. [98] proposed a TDoA-based RTTS for patient tracking in healthcare environments. The system consists of four anchors and two wearable tags embedded in a wristband. The anchors are synchronized, fixed in a wall, and connected through Ethernet via a 24-port Ethernet hub. The nearest anchor for the tags is considered to be the reference anchor, and it is responsible for collecting the TDoA information. All of the data are forwarded to the system server, which is a Raspberry Pi platform (Raspberry Pi Ltd., Cambridge, UK). The server is responsible for calculating the coordinates of the movable tags. The Qorvo DW1000 UWB module has been used in both anchors and tag design, where the STM32F407VET6 and the STM32L041K6U6D (STMicroelectronics, Geneva, Switzerland) microcontroller have been used for anchors and tags, respectively. A mobile-phone- and web-based application can be used for monitoring. Liu et al. [99] proposed a high-precision wireless IPS based on TDoA combined with the Taylor algorithm. The system’s UWB units have the same hardware design and can be programmed as anchors or tags. The unit’s hardware consists of the Qorvo DW1000 and the STM32F103RCT6 (STMicroelectronics, Geneva, Switzerland) microcontroller, which communicates via an SPI bus. The system testing was performed using four fixed synchronized anchors and one movable tag. The test results revealed that the system’s positioning measurement errors do not exceed 10 cm.
The main challenge in implementing TDoA-based IPSs is the need for time synchronization of the used anchors. One possible solution to overcome this challenge is to use a synchronization reference anchor that broadcasts periodic synchronization signals to the other anchors. Several researchers have developed the TDoA method in a way that does not require time synchronization between anchors, reducing the complexity and cost. Xue et al. [100] proposed a deep learning algorithm for TDoA IPS security with missing data and measurement errors called “DeepTAL”. First, the algorithm is trained with the system without the anchors’ synchronization condition, and then it is applied to the TDoA synchronized system in real time. The system was implemented using the Qorvo DW1000 UWB radio transceiver with an ARM cortex M43 STM32f105 (STMicroelectronics, Geneva, Switzerland) microcontroller. The practical tests of the system were implemented using four anchors and one tag. The test results show that the system can predict the TDoA accurately in the presence of measurement errors or missing data. Cho et al. [101,102] proposed a 3D TDoA-based IPS for tracking the workers in hazardous indoor areas of construction sites. The system hardware consists mainly of the commercial Samsung SmartTag+ (Samsung Electronics Co., Ltd., Suwon, Republic of Korea) module. The UWB chip in this tag is the NXP Trimension SR040 (NXP Semiconductors, Eindhoven, The Netherlands), which is powered by a small coin-cell battery and used for tracking objects using new Samsung smartphones with UWB technology. Four SmartTag+ modules were used as fixed anchors for indoor environments, and a Samsung smartphone was used as a mobile tag. The system’s testing showed that the spatial configuration of the anchors and tags with increasing height differences has a significant impact on the accuracy of the 3D indoor localization. The system tests demonstrated an accuracy of 35 cm. Patru et al. [103] proposed a scalable TDoA-based IPS “FlexTDOA”. The system topology is based on passive tags and active fixed anchors, where the anchor clocks’ synchronization is not required; instead, the flexible time-division multiple access (TDMA) scheme is used for the anchor communications. The system hardware was built using the Qorvo DW3000 UWB transceiver with the ARM cortex M4 STM32F429ZIT6 (STMicroelectronics, Geneva, Switzerland) microcontroller for control and data processing. The ranging data were transmitted using a USB 2.0 port, and each unit had the main communication buses, such as UART, SPI, and I2C. The system units used a rechargeable Li-ion battery as a power source and the MCP73830 IC for power management. The UWB unit was configured to operate on channel 5, with 6.5 GHz and a data rate of 6.8 Mb/s. The system was tested using ten anchors and one tag. The test results show that the proposed method has better performance than classical TDoA, with an accuracy of ≈17 cm in LoS and ≈22 cm in NLoS environments. Bottigliero et al. [104] proposed a low-cost TDoA RTLS that does not require time synchronization to reduce the system complexity by using a one-way communication scheme. The tags blink a sequence of 2-nanosecond pulses on a 7.25 GHz carrier frequency, which is received by the anchors with a two-step correlation analysis to calculate the ToA of the tag at each anchor. The hardware of the system has a custom design for the anchors and the tag. The system tests used three anchors and one tag, and the achieved accuracy was ≈10 cm. Pérez-Solano et al. [105] proposed another approach for unsynchronized TDoA IPSs using one-way ranging for time-of-arrival calculations. The system consists of hardware and software parts; the system units include the Qorvo (DecaWave) DWM1000 UWB modules with a 2-millisecond packet transmission time and the Raspberry Pi platform (Raspberry Pi Ltd., Cambridge, UK) for control and processing. The system was tested in two different deployments: the first in a single room, with an accuracy of ≈15 cm, and the second in a multi-room environment, with 56 test points and an accuracy of ≈50 cm. Similar TDoA-based IPSs have been proposed in [106,107,108,109].

5.2.3. Phase Difference of Arrival (PDoA)-Based Indoor Positioning Systems

This method is significant for peer-tracking applications, especially for mobile tracking applications that have to be implemented without relying on a fixed infrastructure. Positioning is determined by calculating the distance and direction of the UWB nodes. Measuring the angle of arrival (AoA) and the time of flight between tags and anchors can be achieved using both angle-of-arrival and two-way ranging algorithms [8]. One or both of the used UWB units should have two antennas to allow for the AoA calculations. Many researchers have used PDoA for IPS implementation. Zhang et al. [110,111] proposed a PDoA-based IPS for tracking the elderly in smart homes. Their work aimed to overcome the NLoS problem in elderly living environments. The coarse location was calculated initially to find the nearest LoS anchors in the NLoS environment, and then the NLoS anchors’ measurement was canceled and only the LoS anchors were used for the elderly location tracking. The system hardware used two different units from Qorvo (DecaWave Limited, Dublin, Ireland): a UWB transceiver, and a PDoA chip combined with a host processing board. The tag had a different design, and it was included in a wearable wristwatch with GPS, Zigbee, and some inertial and biosensors. The system was practically tested with nine fixed anchors in different positions, with LoS and NLoS positions for the tag, and the results showed that the system performed the tracking tasks successfully. Botler et al. [112] proposed four different machine learning models for PDoA IPSs. The models were trained with real-time data from commercial UWB units. The system hardware consisted of the DecaWave Beta-PDoA UWB kit, and the practical testing of the developed models showed that the random forest regressor model had the best performance, with a mean parameter of 8°. Ge et al. [113] proposed a single-anchor RTLS using wrapped PDoA based on an antenna array with arbitrary geometry. The object localization was implemented by measuring the ToF and the wrapped phase of the UWB signals. The localization process included obtaining parameters by running a soft positional information algorithm, which was approximated by the Gaussian mixture model (GMM) to accelerate the filtering process. The system was practically implemented in a single-antenna tag and an eight-antenna anchor connected to the DecaWave DW1000 UWB transceiver, which could calculate the time of arrival and the carrier phase—the main parameters required for implementing PDoA. The system testing results revealed that localization accuracy of ≈8 cm could be achieved. Other PDoA approaches are presented in [114,115,116,117,118,119,120].

5.3. Indoor Positioning Systems for Robotics Applications

The indoor positioning systems of mobile robots are essential and indispensable, especially in swarm robotics, simultaneous localization and mapping (SLAM), and large-scale applications. From the perspective of functionality and practicality, the UWB-based IPS is more suitable than other technologies for robotics applications [121]. In this section of the paper, we will review the most important integrated works and research in this field. We consider the systems that contain all of the main parts of RTTSs, such as the hardware components that allow the indoor tracking and positioning operation with the UWB technology, along with the interactive software to show and monitor the relative location information and object coordinates in a graphical user interface. The integrated system can simulate the physical reality in real time in the software, enabling real-time tracking with the best possible accuracy. Figure 12 gives an example representing the real-time tracking system in a Python-based monitoring program.
There are many works related to this topic. Xu et al. [122] proposed an extensible RTLS for mobile robots. The system consisted of one tag fixed on the mobile robot, five anchors (four fixed and one with an unknown position), a camera, and an IMU. The UWB anchors did not require clock synchronization. This system can be extended by adding a new anchor, where the recursive least squares (RLS) approach is used for the new anchor’s location estimation, and the maximum correntropy Kalman filter (MCKF) is used to fuse the data from the UWB network and IMU. The system’s main hardware included the DecaWave DWM1000 transceivers for the UWB tag and anchors with a small mobile robot equipped with the KY-INS110 IMU unit (Beijing BDStar Navigation Co., Beijing, China), and MYNT EYE D1000-IR-120 stereo camera (MYNT AI, Jiangsu, China). The system tests indicated a 0.137 m root-mean-square error (RMSE) in the positioning location. Li et al. [4] proposed a UWB-based RTLS for mobile mining robots in harsh coal mine environments. The system used both UWB technology for location and IMU sensors for orientation to enhance its positioning accuracy. Four anchors and one tag were used to perform a 3D positioning in the coal mine using the P440 UWB module (TDSR LLC., Petersburg, TN, USA). The MTi-G-710 motion tracker IMU module (Movella Inc., Henderson, NV, USA) was used to provide the robot’s orientation data. The UWB tag and the IMU modules were fixed on the mobile tank robot. The data of both the UWB and IMU modules were fused using the error-state Kalman filter (ESKF). The system tests indicated that a localization accuracy of ≈20 cm can be achieved. Cano et al. [123] proposed a Kalman filter-based IPS for mobile robots. Their work aimed to mitigate the multipath issue of the UWB waves using the M-estimation robust Kalman filter (M-RKF) and leveraging an adaptive empirical variance model. The system hardware used the Qorvo DW1000 modules for both the anchors and tag, where the tag was fixed in a mobile ground robot surrounded by four fixed anchors. The TWR protocol was used for range measurements. The movable tag acquired all of the range measurements at a 280 Hz refresh rate. The practical system tests indicated that the use of M-RKF enhances the system localization performance by 14% in LoS and 31% in NLoS environments. Ranjan et al. [124] developed an IPS for mobile robots by integrating the filtering methods moving average (MVG), Kalman filter (KF), and extended Kalman filter (EKF) with a low-pass filter (LPF). The used hardware included the commercial POZYX UWB positioning system (POZYX, Ledeberg, Belgium) and the open-source ROS-based TurtleBot3 mobile robot (ROBOTIS Inc., Tokyo, Japan). The system testing showed that the extended Kalman filter with a low-pass filter (EKF + LPF) had the lowest root-mean-square error (RMSE) of 40.22 mm on the x-axis, 78.71 mm on the y-axis, and reduced mean absolute error (MAE) percentages of 3.39% for square paths, 4.21% for circular paths, and 6.16% for free paths. Tang et al. [125] proposed an enhanced IPS using human–robot collaboration. This method aims to reduce the number of fixed anchors in classical IPSs by using the mobile robot as a reference anchor of positioning systems, while also enhancing the positioning accuracy using inertial navigation data from pedestrian smartphones. The system was tested in three scenarios, the first with only three fixed anchors and pedestrians holding smartphones. In the second scenario, a mobile anchor (mobile robot with UWB module) was added, which added the LiDAR positioning information to enhance the system’s performance. In the third scenario, only two mobile anchors and two fixed anchors were used. The test results indicated that the best system performance was achieved in the second scenario, with an RMSE of 0.355 m. The authors did not provide technical details about the used hardware components. Alghuraid et al. [126] developed a UWB-based IPS for grid-confined robotic platform position control. The system hardware used the commercial Qorvo MDEK1001 kit, which mainly uses the Qorvo DW1000 UWB transceiver. Eight modules were used as fixed anchors, one was used as a movable tag attached to a Mecanum four-wheel drive robot, and one was used as a listener for providing the streaming coordinates of the UWB system to the monitoring laptop via a USB COM port. The mobile robot was controlled by the Atmel ATmega2560 (Atmel Corporation, San Jose, CA, USA) microcontroller, and the robot communicated with the control station wirelessly using the HC-12 Bluetooth module (Guangzhou Huicheng Information Technology Co., Ltd., Guangzhou, China). The open-source Tera Term terminal software, Arduino IDE (Arduino LLC., Boston, MA, USA), and MATLAB (MathWorks Inc., Natick, MA, USA) were used for data processing and monitoring. The practical tests of the system achieved an X-Y location accuracy of 7 cm. Other robotics-based IPSs are presented in [127,128,129,130,131,132,133,134,135].

5.4. Indoor Positioning Systems for Unmanned Aerial Vehicle Applications

The use of tracking systems for unmanned aerial vehicles is an essential element. This is due to the nature of the tasks that they perform through flight and their specific applications, which are often in open outdoor environments outside buildings and usually use satellite navigation and tracking systems. The need to use these vehicles in indoor environments for tasks that mobile robots cannot perform is a challenge for them, as satellite signals are weak and unusable. Here, the need to use indoor tracking and positioning systems of various types, especially those operating on ultra-wideband waves, arises. The current review paper is limited to indoor tracking and positioning systems, so this chapter will be limited to works that meet this description.
Kefferpütz et al. [136] presented an indoor navigation system for UAVs for fusing data from UWB, IMUs, and barometers using an error-state unscented Kalman filter. The Crazyflie 2.1 (Bitcraze AB, Malmo, Sweden) open-source platform and the Loco Positioning System (Bitcraze AB, Malmo, Sweden), which is based on the DecaWave DWM1000, were used for system implementation. The developed approach was implemented on the UAV MCU and tested in real-time closed-loop flight. Wang et al. [137] proposed an indoor RTLS for the localization of multiple UAVs using maximum likelihood Kalman filtering (MLKF). This work aimed to enhance the localization performance for flying objects in the vertical axis via the anchor’s height level similarity. The work used Flame Wheel 450 (DJI Technology Inc., Shenzhen, China) UAVs with a wheelbase of 450 mm, equipped with an onboard Intel CPU (Intel Corporation, Santa Clara, CA, USA) for running the Ubuntu16 (Canonical Ltd., London, UK) operating system. Four UWB anchors were distributed in four corners of the testing environments and tested with several heights, from 90 cm to 180 cm. The used UWB system operated on channel 1, with 3.5 GHz central frequency and a 110 Kbps sample rate. High-precision motion-capture cameras were used for providing true position and speed. The practical system tests showed that the localization error was reduced by 60% by using the proposed MLKF, with only 20% of the computational time of the classical method. Zhang et al. [138] proposed a precise energy-saving indoor RTTS for UAVs in coal mines using an on-demand trigger algorithm based on an unscented Kalman filter. The filter fuses the data from UWB units and IMU units to achieve on-demand precise tracking (OPT). The P440 UWB module (TDSR LLC., Petersburg, TN, USA) was used to implement the anchors and tag. The Amovlab Z410-4b (Amu Lab-Chengdu Bobei Technology Co., Ltd., Chengdu, China) UAV was used for system implementation and testing, where the UWB tag was fixed on the UAV chassis. The practical tests were implemented in indoor laboratories as well as in coal mine indoor environments. The test results revealed that the proposed method improved the accuracy by 10.3% and 11.4% in indoor laboratory and coal mine environments, respectively, and the energy consumption was reduced by 19.6%. Zhang et al. [139] proposed an indoor UAV formation system based on a long short-term memory (LSTM) neural network. The LSTM network combined with row data of UWB units was used for UAV positioning using historical information for enhancing the system performance. Five Firefighter UAVs (Yifei Intelligent Control Technology Co., Ltd., Tianjin, China) were used to hold the tags of the LinkTrack-P (SZ Nooploop Technology Co., Ltd., Shenzhen, China) positioning system, which also includes four anchors as a base station for practical system testing in indoor environments. The test results indicated that the system achieved a positioning accuracy of 5 cm. Lin et al. [140] proposed a UWB-incorporated visual–inertial odometry (VIO) system for UAV indoor navigation in GNSS-denied environments. Their work aimed to improve localization accuracy by combining the VIO with the ultra-wideband (UWB) positioning technology. A commercial UAV controlled by the Cube Pixhawk2 (CubePilot Pty. Ltd., Breakwater, Australia) and equipped with a 3DM-GX5-25 (LORD Corporation, Williston, VT, USA) IMU and the Intel RealSense D455 depth camera (Intel Corporation, Santa Clara, CA, USA) was used for practical system testing. The LinkTrack S (SZ Nooploop Technology Co., Ltd., Shenzhen, China) UWB-based IPS, which is based on the TDoA ranging approach, was used, including four fixed anchors and a movable tag. The experimental test results showed that the proposed method achieved an accuracy of 41.4 cm, which was better than the individual performance of UWB (46.3 cm) and VIO (1.506 m). Niculescu et al. [141] proposed an energy-efficient 3D indoor RTLS for UAVs. They used UWB ranging with wake-up radio (WUR) technologies to minimize the modules’ power consumption during data exchange between the tags on the UAVs and the anchors. Their approach was composed of a lightweight positioning algorithm, optimal flight strategy, and ranging error correction algorithm. The system hardware consisted of the Crazyflie 2.1 (Bitcraze AB, Malmo, Sweden) UAV with the STM32F405 Cortex-M4 (STMicroelectronics, Geneva, Switzerland) microcontroller, which enables the flight control, the positioning algorithm implementation, and the integration of BMI088 and BMP388 (Bosch Sensortec GmbH, Reutlingen, Germany) IMU units and pressure sensors, as well as the UWB tag for positioning and localization. A custom-designed UWB-Wakeup shield based on the Qorvo DW1000 transceiver was used for UWB anchors and tag implementation at a 6.8 Mbit/s sample rate. The UWB-Wakeup shield also had the CC1200 (Texas Instruments Incorporated, Dallas, TX, USA) radio transceiver for sending on–off-key wake-up signals to the WUR receivers at 868 MHz. The system testing results showed that the localization error was within 28 cm at 1.2 ms MCU processing time, and it was improved by an additional 25% with the error correction algorithm. The energy consumption was determined to be 24 mJ at the sensor node, which is 50 times less compared to a traditional system with the same localization accuracy. Other UAV-based IPSs were proposed in [142,143,144,145,146,147,148].

6. Results

The works selected for this review were investigated for several factors to ensure that they served the purpose of our work, which was to facilitate the study and selection of real-time indoor tracking and positioning systems operating with ultra-wideband technology. The first factor was suitability for the indoor environment. This point is essential because of the limited number of systems that operate efficiently in indoor environments, due to the difficulty of UWB wave transmission between IPS units without passing through the obstacles that are a feature of these places, such as walls, furniture, doors, windows, etc. The second factor that we emphasized was the possibility of using the selected works to track moving objects such as robots, drones, etc., as well as the ease of integrating them with these platforms. The third factor that we focused on was the ability of the selected works to display the location of the objects to be tracked, whether using user graphical interfaces, modeling software, or any programs that can be used to monitor moving objects in real time.
We summarized the topology and ranging technique, the used UWB chip/module, the used sensors (hybrid), the used UWB frequency, the optimization algorithm/filter, the application, the performance improvement, the system positioning accuracy, the monitoring program/software, and the work reference number and year of the included IPSs/RTLSs, as shown in Table 3.

7. Discussion

The results listed in Table 3 provide a clear picture of the prevailing trend in the implementation of indoor tracking and positioning systems. The data provide important information about the most popular available UWB hardware components, modules, and units that can be relied upon to implement the required IPSs in real time. The dominance of the products of Qorvo Inc. (DecaWave) in the market of this field is clearly evident, as the percentage of works that relied on direct components from this company reached approximately 73.5% (108/147) of the systems included in the current work—especially the popular transceiver DW1000, which is the core of 101 out of 108 presented IPSs. There are also some systems and products with private brands that rely on the use of DW1000 units in their structure. This spread is due to several factors, including the scarcity of similar alternatives, its low price, and its high accuracy, resulting in the development of many commercial UWB systems. The new and improved generation DW3000 has been launched, which has many improvements, especially in the field of energy consumption, where it consumes 50% less energy than DW1000 [243]. It also supports higher SPI bus speed and can work on UWB channel 9, making it a better choice for new designs. Figure 13 explains the distribution of the used UWB modules in this study.
Several topologies have been used to measure the distances between the IPS UWB components to allow the positioning and localization algorithm to calculate the exact location and provide the coordinates of the tracked movable object. In the present work, the TWR method had the highest implementation in IPSs, at 78/147 (see Figure 14). This is the result of this method’s simplicity of implementation, without the need for anchors to synchronize complex hardware and software requirements. The TDoA approach was used in 52.35% of the included systems, while the PDoA and hybrid implementations accounted for 6.4% and 11.8%, respectively.
Some IPSs use other sensors besides the UWB technology to enhance the system performance using sensor fusion algorithms such as Kalman filters and ANNs. In the present work, 30% of the proposed IPSs used different types of sensors with UWB, such as IMUs, vision sensors/cameras, encoders, LiDAR/IR, acoustic/ultrasound, GPS, Bluetooth, and Wi-Fi (see Figure 15).
Many algorithms and filters have been used to reduce position errors caused by measurement errors in ultra-wideband technology, especially when passing through obstacles in non-line-of-sight environments. These algorithms and filters are also used to fuse data from more than one source when using indoor positioning systems operating with different types of technologies and sensors. Figure 16 explains the distribution of the used algorithms and filters in the proposed systems.
Indoor positioning and tracking systems can be used in many applications, whether for personal uses such as tracking and monitoring systems for the disabled and the elderly in the health field, or to track the movement of valuable private property. They can also be used for industrial applications, resource management, and in warehouses, e.g., for tracking the movement of goods, forklifts, robots, and unmanned ground and air vehicles. Figure 17 explains the most important applications for the included references.
The accuracy data in Table 3 show that the accuracy varies between the different systems. This is logical because the testing environments and conditions differ in terms of the numbers of anchors and tags used, as well as the types of algorithms used to calculate the distances between the system units or to reduce and filter errors during the identification and tracking of moving objects. The largest error rate recorded was 89.2 cm. Figure 18 shows the percentage range of errors recorded for the systems included in this study.
Despite the importance of monitoring programs that convert ultra-wideband unit measurements into distances and plot the coordinates and locations of moving objects, many works lack information on how this part was implemented and which software or programming languages were used. The use of MATLAB was noted in 7/147 works, while Python was used in 12/147 works. Robot tracking systems and SLAM systems were used in 16 of the works presented here, while various programs were used in 6/147 works. The type of program or language used was not mentioned in the remaining works.

8. Conclusions

Ultra-wideband technology offers attractive features such as its accuracy, robustness, and ability to penetrate obstacles, making it one of the preferred choices for implementing real-time indoor positioning and tracking systems. In this review, we investigated the real-time indoor positioning, tracking, and localization systems developed during the past 5 years. A total of 147 research papers were included this review of real-time positioning systems using different modules, units, and chips from different vendors, with systems implemented using different methods and algorithms. Basic information was extracted from the works included in the survey period, such as the type of hardware used and its origin, methods of implementing the systems, types of sensors and technology used, algorithms and filters to improve performance, and the accuracy of the measurements obtained. This review ensures a clear picture of the types of methods and algorithms used to implement these systems in real time. The selected works were addressed and discussed from several perspectives in terms of the hardware components used, the frequency range and ultra-wideband channels, the types of topologies implemented, the filters and algorithms to improve the performance, and the accuracy of the obtained performance, which we believe are important for any researcher in this field to build an initial idea about how to choose the appropriate type and method to implement this process in real time.

Author Contributions

Conceptualization, M.F.R.A.-O., T.R. and K.T.; formal analysis, M.F.R.A.-O. and K.T.; writing—original draft preparation, M.F.R.A.-O.; methodology, M.F.R.A.-O., S.J. and K.T.; writing—review and editing, M.F.R.A.-O. and K.T.; visualization, M.F.R.A.-O.; supervision, K.T.; project administration, T.R. and K.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research is part of the Synergy Project ADAM (Autonomous Discovery of Advanced Materials), funded by the European Research Council (grant number: 856405).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank the European Research Council (ERC) for funding the Autonomous Discovery of Advanced Materials (ADAM) project.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

ARPFAdaptive robust particle filtering
ANNArtificial neural network
BLEBluetooth Low Energy
CRLBCramér–Rao lower bounds
CNNConvolutional neural network
DFDebiasing filter
ECAEfficient channel attention
FNNFuzzy neural network
GPSGlobal Positioning System
BYOPSBring Your Own Positioning System
AltDS-TWRAlternative double-sided two-way ranging
SDS-TWRSymmetric double-sided two-way ranging
EDS-TWRExtended double-sided two-way ranging
EFIR-RTSExtended finite-impulse response-based Rauch–Tung–Striebel smoother
DS-TWRDouble-sided two-way ranging
DBLDDynamic best link discovery
DNNDeep neural network
FLAFive-line approximate
IPSIndoor positioning system
ISARInverse synthetic-aperture radar
IGNIterative Gauss–Newton
RFRadio frequency
KFKalman filter
CKFCubature Kalman filter
KNNk-Nearest neighbors
LoSLine-of-sight
LSLeast squares
LSTMLong short-term memory
LinHPSLinear hyperbolic positioning system
MLMachine learning
MALMulti-algorithm
MPGAMulti-population genetic algorithm
MCC-VCMaximum correntropy criterion with variable center
MCAMultipath component analysis
NLoSNon-line-of-sight
NNNeural network
PFParticle filter
REKFRobust extended Kalman filter
RFIDRadio frequency identification
RSSIReceived signal strength indicator
RNNRecurrent neural network
SARSynthetic-aperture radar
SWFSliding window filtering
STVBF-DOSkew-t variational Bayes filter-disturbance observer
TDoATime difference of arrival
ToFTime of flight
TWRTwo-way ranging
UWBUltra-wideband
UAVUnmanned aerial vehicle
PAALPassive anchor-assisted localization
PDoAPhase difference of arrival
PZSPredictive zone selection
PDoPPosition dilution of precision
GDoPGeometric dilution of precision
CWCSCascaded wireless clock synchronization
KFKalman filter
ROSRobot operating system
WAKFWeighted adaptive Kalman filter
WIPWeighted indoor positioning
UKFUnscented Kalman filter
UCASUWB control and analysis suite
EKFExtended Kalman filter
TDMATime-division multiple access
TDoATime difference of arrival
SMASlime mold algorithm
VGICPVoxelized generalized iterative closest point
WHFFAWeighted hybrid filter following algorithm

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Figure 1. UWB and other RF technologies’ frequencies and power spectral densities.
Figure 1. UWB and other RF technologies’ frequencies and power spectral densities.
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Figure 2. Flowchart of the selection process.
Figure 2. Flowchart of the selection process.
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Figure 3. Triangulation IPS positioning technique.
Figure 3. Triangulation IPS positioning technique.
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Figure 4. Trilateration IPS positioning technique.
Figure 4. Trilateration IPS positioning technique.
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Figure 5. Multilateration positioning technique in a 3D environment.
Figure 5. Multilateration positioning technique in a 3D environment.
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Figure 6. SDS-TWR tag–anchor signal propagation times.
Figure 6. SDS-TWR tag–anchor signal propagation times.
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Figure 7. Topology of the TDoA positioning algorithm.
Figure 7. Topology of the TDoA positioning algorithm.
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Figure 8. PDoA positioning algorithm.
Figure 8. PDoA positioning algorithm.
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Figure 9. Signal propagation path for UWB IPSs in (a) LoS and (b) NLoS.
Figure 9. Signal propagation path for UWB IPSs in (a) LoS and (b) NLoS.
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Figure 10. TWR IPS topologies: (a) anchor-based; (b) tag-based.
Figure 10. TWR IPS topologies: (a) anchor-based; (b) tag-based.
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Figure 11. TDoA IPS topologies: (a) TDoA; (b) RTDoA.
Figure 11. TDoA IPS topologies: (a) TDoA; (b) RTDoA.
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Figure 12. IPS: (a) real-time testing environment; (b) 2D simulated environment with real-time object tracking using Python [80].
Figure 12. IPS: (a) real-time testing environment; (b) 2D simulated environment with real-time object tracking using Python [80].
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Figure 13. Distribution of the used UWB modules.
Figure 13. Distribution of the used UWB modules.
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Figure 14. Distribution of the used UWB topologies.
Figure 14. Distribution of the used UWB topologies.
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Figure 15. Distribution of the sensors and supporting technologies besides the UWB.
Figure 15. Distribution of the sensors and supporting technologies besides the UWB.
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Figure 16. Distribution of the used algorithms and filters.
Figure 16. Distribution of the used algorithms and filters.
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Figure 17. The main applications of the proposed references.
Figure 17. The main applications of the proposed references.
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Figure 18. The percentage range of errors for the included systems.
Figure 18. The percentage range of errors for the included systems.
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Table 1. Commercial UWB modules and chips for IPSs.
Table 1. Commercial UWB modules and chips for IPSs.
VendorChip/ModuleEstimation
Method		LoS
Accuracy
(± cm/°)		Operation
Range (m)		Frequency
Range (GHz)		UWB ChannelsApplicationReference
QorvoDWM1000TWR, TDoA15 in 2D,
30 in 3D		3003.5–6.51–5, 7IPS, asset, employer[42]
QorvoDW3110, DW3210TWR, TDoA15 in 2D,
30 in 3D		506.5, 85, 9IPS, asset, employer[32,43]
QorvoDW3120, DW3220TWR, TDoA, PDoA15 in 2D,
30 in 3D		506.5, 85, 9IPS, asset, employer[44,45]
NXPSR150AoA, TWR, TDoA10/3506.24–8.245, 6, 8, 9Industrial IoT, RTLS[35]
NXPSR40TWR, TDoA10506.24–8.245, 6, 8, 9IoT battery-based, IPS[46]
MicrochipATA8350TDoA15-6.2–7.85–8IoT battery-based [47]
MicrochipATA8352TDoA4-6.2–8.35–9IoT battery-based[48]
Infineon3DB6830TWR101206-85–9Industrial IoT[39]
SparkSR1010TWR301003.1–61–5IoT applications, Asset and individual tracking[49]
SparkSR1020TWR301006–9.36–9Industrial IoT[49]
STMMOD1TWR106003.25–4.751, 2, 3, 4RTLS, asset, employer [41]
Table 2. Commercial UWB-based solutions for IPSs.
Table 2. Commercial UWB-based solutions for IPSs.
VendorAlgorithm/TopologyAccuracy
(± cm)		Operation Range (m)Frequency
Range (GHz)		ApplicationReference
Inpixon TWR/AoA40 506.4Asset, employer[50]
Eliko TWR/TDoA5070NARTLS[51]
Litum TWR/TDoANA703.25–6.75Asset, employer, forklift[52]
WOXU ToF/TDoA10NA3.24–6.74RTLS[53]
Sewio TDoA30503–7Asset, employer, forklift[54]
KKM TDoA30 NANAAsset[55]
Kinexon TDoA/AoA20NA6.25–6.75RTLS[56]
PozyxToF30503.5–6.5People, assets, vehicles[57]
ZebraToF302006.35–6.75Transportation, warehouse[58]
Tsingoal ToF/TDoA30NANARTLS[59]
Ubisense TDoA/AoA30656–7Asset[60]
Kathrein ToF/TDoA10403–10Vehicle, forklift[61]
Ubudu TDoA/TWR20NANAPortable tracking device[62]
Tracktio TDoA301503–7Worker, warehouse[63]
LeisureToF/TDoA11003.1–10.6Vehicles[64]
MK TDoA/AoA7NA6.5–8RTLS[65]
BeSpoon TWR108803.99RTLS[66]
Table 3. Summary of selected IPSs.
Table 3. Summary of selected IPSs.
TopologyUWB Chip/ModuleMulti-SensorUWB
Frequency/Channel		Optimization
Algorithm/Filter		ApplicationImprovement (%)/
Accuracy (cm)		MonitoringReference No., Year
SS-TWRDW1000UWB5 GHzKF, PFIPS13 cmMATLAB[79], 2023
DS-TWRDW3000UWB6.5 GHz, CH5Trilateration, LSMIPS, Robotic50 cmPython[80], 2024
SS-TWRDW1000UWB3.5–6.5 GHzTrilaterationIPS14 cmCustom[81], 2020
DS-TWRDW1000UWB3.5–6.5 GHzKFIPS8%Custom[82], 2021
TWRDW1000UWB+IMU3.5–6.5 GHzDUKF, RMSEIPS6 cmMATLAB[83], 2024
AltDS-TWR, TDoADW-EVK1000UWB3.5–6.5 GHzMultilaterationIPS-Custom[84], 2020
SDS-TWR, AltDS-TWRElikoUWB-RTLSUWB3.5–6.5 GHzActive–passive methodRTTS20%R Language[85], 2022
TWRWi-Pos UWBUWBCH5, 6.5 GHzKFIPS50%, 15 cmCustom[86], 2024
TDoADW1000UWB3.5–6.5 GHzTrilaterationRTTS9 cmWeb-based
application		[98], 2022
TDoADW1000UWB3.5–6.5 GHzTaylor algorithmIPS10 cmCustom[99], 2022
TDoADW1000UWB3.5–6.5 GHzLSTM, DeepTALIPS20 cmCustom[100], 2019
TWR, TDoADW3000UWBCH5, 6.5 GHzEKFIPS38%, 22 cmCustom[103], 2023
TDoACustomIR, UWB7.25 GHzMultilateration, median, mean F.RTLS10 cmCustom[104], 2021
TDoADW1000UWB3.5–6.5 GHz-IPS15 cmCustom[105], 2020
PDoACustomUWB, IMU, GPS6.5 GHzChan andconvex methodIPS45 cmCustom[110,111], 2021
AoA, PDoADecaWave Beta-PDoAUWB3.5–6.5 GHzTriangulationIPS-KDE plots[112], 2022
wrapped PDoADW1000UWB3.5–6.5 GHzGMMRTLS8 cmCustom[113], 2022
TWRDW1000UWB, IMUCamera3.5–6.5 GHzMCKFRobotics, RTLS13.7 cmSLAM[122], 2019
TWRP440UWB, IMU4.3 GHzESKFRobotics, RTLS20 cmCustom[4], 2020
TWRDW1000UWB3.5–6.5 GHzM-RKFIPS, robotics31%, 30 cmCustom[123], 2023
TDoAPozyx UWBUWB,
LiDAR		CH5, 6.5 GHzMVG, EKF, LPFRTLS,
robotics		6.16%, 8 cmPython[124], 2024
TWRCustomUWB, LiDAR–EKF, PDRIPS, robotics47.8 cmCustom[125], 2024
TWRMDEK1001UWB LiDAR3.5–6.5 GHzTrilaterationIPS, robotics7 cmCustom[126], 2021
TWRDW1000UWB, IMU3.5–6.5 GHzUKFIPS, UAV15 cmCustom[136], 2022
TWRCustomUWBCH1, 3.5 GHzMLKFRTLS, UAV60%, 22.4 cmCustom[137], 2021
TWRP440UWB, IMU4.3 GHzET-EKFIPS, UAV10.3%,Gazebo platform[138], 2022
TDoALinkTrack-PUWB, IMU3.5 –6.5 GHzLSTM-RNN,
EKF		IPS, UAV, robotics5 cmCustom[139], 2023
TDoALinkTrack-SUWB, IMU, camera3.5–6.5 GHzCustomIPS, UAV, robotics41.4 cmSLAM[140], 2023
TWRDW1000UWB, IMU3.5–6.5 GHzMultilaterationRTLS, IPS, UAV25%Custom[141], 2023
PDoADW3000UWBCH5, 6.5 GHzDistance adaptive weightingIPS36 cmCustom[149], 2023
TDoALinkTrack UWB, IMU, camera3.5–6.5 GHz-IPS, UAV5 cmCustom[143], 2020
TDoADW1000UWB3.5–6.5 GHzCWCSIPS, robotics20 cmCustom[150], 2024
TDoADW1000UWB, IMU3.5–6.5 GHzTrilateration, Kalman F.IPS, RTLS94.7%, 8.25 cmCustom[151], 2022
AoA, TDoADW1000UWB, IMU3.5–6.5 GHzPAAL, UKFRTLS12%, 12.5 cmCustom[152], 2021
TWR, TDoADW1000UWB3.5–6.5 GHzSpoofing detectionRTLS30 cmCustom[153], 2024
TDoADW1000UWB4 GHz, 6 GHzAperture-coupled patchIPS, RTLS, UAVSize of an appleCustom[154], 2022
DS-TWRMDEK1001UWB3.5–6.5 GHzTrilaterationRTLS23.76 cmCustom[155], 2021
TDoAEVK1000UWB3.5–6.5 GHzTDMARTLS, Robotic<30 cmPython[156], 2019
AltDS-TWRTREK1000UWB3.5–6.5 GHzMultilateration, KFRTLS<28 cmCustom[157], 2019
SDS-TWRMAX2000/
DW1000		UWB, IMU3.5–6.5 GHzEKFRTLS, IPS38.24%, 11 cmCustom[91], 2021
DS-TWR, TDoADW1000UWB3.5–6.5 GHzKFIPS10 cmMATLAB[90], 2022
SS-TWRDW3000UWBCH5, 6.5 GHzTrilateration, WAKFIPS20.84%, 25 cmCustom[158], 2023
EDS-TWRDW1000UWB3.5–6.5 GHzEKFIPS80.53%, 51.8 cmCustom[92], 2024
TWR-TDoADW1000UWB3.5–6.5 GHzMPGARTLS11.21 cmPython[159], 2022
TDoADW1000UWB3.5–6.5 GHzMLIPS29%, 46.6 cmCustom[160], 2021
TDoADW1000UWB3.5–6.5 GHzMCC-VCIPS42 cmMATLAB[161], 2023
TDoATrimension NXP SR040 UWB, Bluetooth6–8.5 GHzTrilateration, LSIPS35 cmSamsung’s app[101,102], 2024
TDoADW1000UWB3.5–6.5 GHzWIP, DNN, LSTMIPS8.1 cmCustom[162], 2021
TDoADW1000UWB3.5–6.5 GHz-IPS26 cmCustom[163], 2019
TDoADW1000UWBCH5, 6.5 GHzEKFIPS<50 cmCustom[164], 2020
TDoADW1000UWB, IMU3.5–6.5 GHzUKFIPS34 cmCustom[165], 2019
TDoAMDEK1001UWB3.5–6.5 GHzLS, KFIPS15 cmSLAM[97], 2022
TDoADW1000UWB3.5–6.5 GHzLinHPSIPS10 cm Custom[166], 2022
TDoADW1000UWB3.5–6.5 GHzPZS, DBLDIPS≈27 cmSLAM[17], 2021
TDoA, FDoADW1000UWB3.5–6.5 GHzWLS, KFIPS≈60 cmCustom[107], 2021
SS-TWR, TDoA, FDoACustomUWBCH5, 6.5 GHzBYOPSIPS, robotics40 cmUCAS[167], 2023
TDoADW1000UWB3.5–6.5 GHz-IPS, robotics30 cmCustom[108], 2021
TDoADW1000UWB3.5–6.5 GHzTrilaterationIPS10 cmPython[168], 2023
TDoANXP-SR100T, UWB6–8.5 GHz, CH5, CH9KF, RNNIPS31%Custom[169], 2023
TDoADW1000UWB3.5–6.5 GHzEKFRTLS17 cmC language[109], 2019
TDoADW1000UWB3.5–6.5 GHzSWFIPS20 cmCustom[170], 2020
TDoADW1000UWB3.5–6.5 GHzFLAIPS17.6 cmCustom[171], 2019
TDoADW1000UWB3.5–6.5 GHzLSIPS8 cmCustom[172], 2024
TDoADW1000UWB3.5–6.5 GHzEKFRTLS19 cmCustom[173], 2019
TDoADW1000UWB, IMU3.5–6.5 GHzDead Reckoning, KFIPS, robotics10 cmCustom[174], 2022
TDoADW1000UWB3.5–6.5 GHzARPF, IPS50%Custom[175], 2023
TDoADW1000UWB3.5–6.5 GHzTriangulationIPS9 cmCustom[176]. 2022
TWR, TDoADW1000UWB3.5–6.5 GHz-IPS30 cmCustom[177], 2023
TDoADW1000UWB3.5–6.5 GHzEKFIPS30 cmCustom[178], 2022
TDoADW1000UWB3.5–6.5 GHzMultilateration, DF, EKFIPS15.8 cmCustom[179], 2022
TDoADW1000UWB3.5–6.5 GHzEKFIPS30.3 cmCustom[180], 2023
TWRDW1000UWB3.5–6.5 GHzMALIPS20 cmCustom[181], 2022
TWRDW1000UWB, GPS3.5–6.5 GHzTrilaterationIPS11.8 cmCustom[182], 2021
TDoADW1000UWB3.5–6.5 GHzIGNRTLS50 cmCustom[183], 2020
TWRDW1000UWB, IMU3.5–6.5 GHzUKFIPS50.28%, 48.7 cmCustom[184], 2024
TWRDW1000UWB, IMU3.5–6.5 GHzTrilateration, EKFRTLS31 cmUnity software[185], 2023
TWR DW1000UWB, LiDAR3.5–6.5 GHzVGICPIPS, robotics40 cmSLAM[186], 2022
TWR DW1000UWB3.5–6.5 GHzMCAIPS, robotics30 cmCustom[187], 2021
TWRP440UWB, IMU4.3 GHzREKFIPS56 cmCustom[188], 2022
TWR DW1000UWB3.5–6.5 GHzEKFIPS60.61%, 10 cmCustom[189], 2020
TDoADW1000UWB3.5–6.5 GHzEKFIPS, robotics, UAV42.09%, 14.1 cmCustom[190], 2021
TWR DW1000UWB3.5–6.5 GHzKFIPS, robotics16 cmCustom[191], 2024
TWRPozyx UWBUWB3.5–6.5 GHzEKFRTLS, robotics20 cmROS[192], 2020
TWRMini 3sUWB, LiDAR3.5–6.5 GHzEKFIPS, robotics<40 cmSLAM-ROS[193], 2019
DS-TWRDW1000UWB, IMU3.5–6.5 GHzKFIPS7.58 cmCustom[194], 2020
TWRDW1000UWB, IMU3.5–6.5 GHzEKFIPS, robotics, UAV60%, 48 cmCustom[195], 2020
TWRSamsung
UM100		UWB, camera3.1–8.976 GHzEKFIPS20 cmSLAM[196], 2020
TWRPozyx UWBUWB,
encoder		3.5–6.5 GHzEKFRTLS, robotics6 cmCustom[135], 2023
TWRPozyx UWBUWB, LiDAR, IMU3.5–6.5 GHzEKFRTLS, robotics10 cmSLAM-ROS[128], 2020
TWR DW1000UWB3.5–6.5 GHzCRLBIPS, robotics12 cmCustom[129], 2023
TDoA DW1000UWB3.5–6.5 GHzEKFIPS, robotics54 cmCustom[133], 2020
TDoA DW1000UWB3.5–6.5 GHzNNIPS, robotics89.2 cmCustom[197], 2024
TWRDW1000UWB3.5–6.5 GHzEFIR-RTSIPS, robotics25.35%, 16 cmSLAM[198], 2022
TWRDW1000UWB, LiDAR3.5–6.5 GHzUKFIPS, robotics, UAV34 cmSLAM[199], 2022
TDoA DW1000UWB3.5–6.5 GHz-IPS, robotics, UAV34.6 cm Custom[142], 2021
TWRDW1000UWB3.5–6.5 GHzMultilateration IPS, UAV28 cmCustom[141], 2023
TWRPozyx UWBUWB, LiDAR, IMU3.5–6.5 GHzEKFIPS, UAV39 cmSLAM[200], 2022
TWRDW1000UWB3.5–6.5 GHzEKFIPS, UAV32%, 6.9 cmCustom[146], 2024
SS-TWRDW1000UWB3.5–6.5 GHzMultilateration, GDoPIPS, robotics, UAV15 cmSLAM[201], 2022
TWRDW1000UWB, IMU3.5–6.5 GHzEKFRTLS, UAV15 cmCustom[202], 2023
TDoALinkTrack-SUWB, IMU, camera3.5–6.5 GHzUWB odometryconstraintIPS, UAV43.4 cmSLAM[203], 2022
TWRDW1000UWB, IMU3.5–6.5 GHzEKFIPS, UAV20.8 cm Custom[145], 2022
TWRDW1000UWB3.5–6.5 GHzEKFIPS, UAV27.8 cmCustom[204], 2023
TWRDW1000UWB3.5–6.5 GHzMultilateration IPS, UAV10 cmCustom[144], 2020
TDoALinkTrack-SUWB, IMU3.5–6.5 GHzEKFIPS, UAV3.8 cmCustom[148], 2023
TDoA DW1000UWB3.5–6.5 GHzSTVBFIPS, UAV42.0%, 40 cmCustom[205], 2022
TWRDW1000UWB, IMU3.5–6.5 GHzTrilateration, KFIPS, UAV10 cmC++[206], 2021
TWRDW1000UWB, IMU, camera3.5–6.5 GHzOutlier rejection algorithmIPS, UAV20%, 14 cmSLAM[207], 2021
TDoALinkTrack-SUWB, IMU, camera3.5 –6.5 GHzCNN, LSTMIPS, UAV42 cmCustom[208], 2023
TWRPozyx UWBUWB3.5–6.5 GHzGDoPIPS, UAV20 cmCustom[209], 2022
TDoA DW1000UWB3.5–6.5 GHzMultilateration IPS, UAV30 cmCustom[210], 2023
TWR DW1000UWB3.5–6.5 GHzANNRTLS26 cmCustom[211], 2024
TWR DW1000UWB3.5–6.5 GHzLSIPS, robotics20 cmCustom[212], 2023
TDoA DW1000UWB3.5–6.5 GHzDoPIPS44 cmCustom[213], 2022
TDoALinkTrackUWB,
LiDAR		3.5–6.5 GHzPFIPS, robotics31.2 cmCustom[131], 2021
TDoA DW1000UWB3.5–6.5 GHzPDoPIPS20 cmCustom[214], 2021
DS-TWRDW1000,
DW3000		UWB3.5–6.5 GHzMultilateration IPS40 cmMATLAB[215], 2024
SDS-TWRDW1000UWB3.5–6.5 GHzTrilaterationRTLS10 cmCustom[216], 2023
DS-TWRDW1000UWB3.5–6.5 GHzEKF, LSTMIPS50%, 37 cmMATLAB[217], 2023
AoA, TDoA DW1000, Ubisense D4UWB3.5–6.5 GHzEKF, PFRTLS40 cmCustom[218], 2020
TWR DW1000UWB3.5–6.5 GHzMultilateration RTLS53 cmPython[219], 2023
TWR DW1000UWB
LiDAR		3.5–6.5 GHzTrilateration, LSIPS, robotics16.34 cmPython[220], 2024
TWR DW1000UWB3.5–6.5 GHzLS, ECAIPS17 cmPython[221], 2023
TWRLinTrackUWB, IMU3.5–6.5 GHzEKFIPS, UAV51 cmCustom[222], 2021
TWR DW1000UWB3.5–6.5 GHzANNIPS88.45%Custom[76], 2022
TWR DW1000UWB, IMU3.5–6.5 GHzPF, EKFIPS36.15%, 71.5 cm MATLAB[223], 2024
TWR DW1000UWB3.5–6.5 GHzCNNIPS7.35 cmCustom[224], 2022
TWR DW1000UWB
LiDAR		3.5–6.5 GHzEKF, PFIPS, robotics7.2 cmSLAM[225], 2023
TWR DW1000UWB3.5–6.5 GHzTrilateration, KNN IPS4.71 cmCustom[226], 2021
TWR DW1000UWB3.5–6.5 GHzTrilateration, UKFIPS4 cm Custom[227], 2024
TWR DW1000UWB, Wi-FiCH12, 3.993 GHzKNNIPS69.8%, 30 cmCustom[228], 2024
TWRPozyx UWBUWB3.5–6.5 GHzTrilaterationIPS30 cmPython[229], 2024
TDoA DW1000UWB3.5–6.5 GHzTriangulation, KF IPS68%, 10 cmWeb/mobile application[230], 2023
TWRPozyx UWBUWB3.5–6.5 GHzANN, KNNIPS34%, 31.6 cmPython[231], 2022
ADS-TWRDW1000UWB3.5–6.5 GHzMultilateration, EKFIPS-Python[232], 2023
TWRPozyx UWBUWB, Wi-Fi3.5–6.5 GHzTrilaterationIPS87 cmPython[233], 2022
TWR DW1000UWB3.5–6.5 GHzSMAIPS6.69 cmCustom[234], 2022
TWR DW1000UWB3.5–6.5 GHzTrilateration, KFIPS, robotics38%, 12.4 cm Custom[235], 2022
TWR DW1000UWB3.5–6.5 GHzWHFFAIPS4 cmCustom[236], 2023
TWR DW1000UWB, IMU3.5–6.5 GHzFNNIPS25 cmCustom[237], 2023
DS-TWRDW1000UWB, IMU3.5–6.5 GHzMultilateration, LS, EKFIPS20 cmCustom[238], 2022
TWR DW1000UWB, IMU3.5–6.5 GHzTrilaterationIPS20 cmCustom[239], 2022
TDoA DW1000UWB3.5–6.5 GHzTrilaterationIPS33.4 cmCustom[240], 2019
TWR DW1000UWB3.5–6.5 GHzLS, ChanIPS57.6%, 15.7 cmPython[241], 2022
AoA, PDoADW1000UWB, IMU3.5–6.5 GHzTriangulationIPS10 cmCustom[242], 2021
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MDPI and ACS Style

Al-Okby, M.F.R.; Junginger, S.; Roddelkopf, T.; Thurow, K. UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review. Appl. Sci. 2024, 14, 11005. https://doi.org/10.3390/app142311005

AMA Style

Al-Okby MFR, Junginger S, Roddelkopf T, Thurow K. UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review. Applied Sciences. 2024; 14(23):11005. https://doi.org/10.3390/app142311005

Chicago/Turabian Style

Al-Okby, Mohammed Faeik Ruzaij, Steffen Junginger, Thomas Roddelkopf, and Kerstin Thurow. 2024. "UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review" Applied Sciences 14, no. 23: 11005. https://doi.org/10.3390/app142311005

APA Style

Al-Okby, M. F. R., Junginger, S., Roddelkopf, T., & Thurow, K. (2024). UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review. Applied Sciences, 14(23), 11005. https://doi.org/10.3390/app142311005

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