Cung Lian Sang

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

This dissertation addresses a bidirectional scheme of Ultra-Wideband (UWB)-based localization system, which is generally overlooked in the literature causing its potential overshadowed. The bidirectional scheme is one of the three design integration processes usually applied in UWB-based localization systems. The key property of the bidirectional UWB scheme is its ability to act as both navigation and tracking tasks within a single localization scheme. Conventionally, the perspective of navigation and tracking in wireless localization systems is treated separately, because distinct methodologies were typically required in the implementation process. The ability to combine two unique positioning perspectives (i.e., navigation and tracking) within a single scheme is, indeed, a paradigm shift in the way location-based services are observed in the literature. Much to the author’s surprise, there are no well-documented books or research articles related to the bidirectional UWB localization system. Thus, this dissertation attempts to serve as a complement to the mentioned gap in the literature. In this dissertation, the bidirectional UWB localization scheme was tackled by dividing the system implementation process into several sectors. Then, the methodologies applicable in each sector were rigorously evaluated in order to give the readers insightful knowledge, findings, and recommendations. Regarding this, the concept of the bidirectional UWB localization scheme and its implementation process were thoroughly explained by comparing it with the typical unidirectional schemes. Concerning the ranging sector of the bidirectional UWB scheme, this dissertation demonstrated the misconception widely practiced in literature in terms of the two-way ranging technique. Moreover, the dissertation suggested a better method compared to the conventional one, which could be used as a baseline or de facto standard for comparing or bench-marking different two-way ranging schemes. The claim was supported by the experimental results rigorously evaluated by using analytical methods, numerical simulation, and real-world experimental data. Moreover, the comprehensive benchmark of five location estimation algorithms for the bidirectional UWB localization system was conducted in the dissertation. The five algorithms were evaluated based on the Bayesian framework and the detailed implementation process was regarded as an important aspect because the literature is generally lacking it, especially for the use-case of the UWB technology. The evaluation results showed that the linear positioning algorithms gave excellent performance in scenarios such as static conditions under a direct path signal. In contrast, the nonlinear techniques appeared to be better at resisting abrupt changes during measurements as well as the scenarios in non-direct path signals. Furthermore, the identification and mitigation of errors produced by non-direct path signals in UWB were addressed. Concerning this, a novel mitigation technique was proposed in the thesis whereas the feasibility of the identification process was evaluated using machine learning methods. The classification procedure was considered as a multi-class problem, which is opposed to the typical binary class approach in literature. The results showed that the machine learning techniques are very promising compared to the conventional ones for identification of the non-direct path signals in UWB localization.

In ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight (LOS), non-line-of-sight (NLOS), and multi-path (MP) conditions is important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS, or MP). However, the major contributions in the literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. However, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the three mentioned classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental dataset. The dataset was collected in different conditions in different scenarios in indoor environments. Using the collected real measurement data, we compared three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results showed that applying ML methods in UWB ranging systems was effective in the identification of the above-three mentioned classes. Specifically, the overall accuracy reached up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it was 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we provide the publicly accessible experimental research data discussed in this paper at PUB (Publications at Bielefeld University). The evaluations of the three classifiers are conducted using the open-source Python machine learning library scikit-learn.

The Two-Way Ranging (TWR) method is commonly used for measuring the distance between two wireless transceiver nodes, especially when clock synchronization between the two nodes is not available. For modeling the time-of-flight (TOF) error between two wireless transceiver nodes in TWR, the existing error model, described in the IEEE 802.15.4-2011 standard, is solely based on clock drift. However, it is inadequate for in-depth comparative analysis between different TWR methods. In this paper, we propose a novel TOF Error Estimation Model (TEEM) for TWR methods. Using the proposed model, we evaluate the comparative analysis between different TWR methods. The analytical results were validated with both numerical simulation and experimental results. Moreover, we demonstrate the pitfalls of the symmetric double-sided TWR (SDS-TWR) method, which is the most highlighted TWR method in the literature because of its highly accurate performance on clock-drift error reduction when reply times are symmetric. We argue that alternative double-sided TWR (AltDS-TWR) outperforms SDS-TWR. The argument was verified with both numerical simulation and experimental evaluation results.

In absence of clock synchronization, Two-Way Ranging (TWR) is the most commonly used technique for measuring the distance between two wireless transceivers. The existing time-of-flight (TOF) error estimation model, the IEEE 802.15.4-2011 standard, is specifically based on clock drift error. However, it is insufficient when an in-depth comparative analysis of different TWR methods is required. In this paper, we propose an extended TOF error estimation model for TWR methods, based on the IEEE 802.15.4 standard. Using the proposed model, we perform an analytical study of TOF error estimation among different TWR methods. The model is validated with numerical simulation results. Moreover, we demonstrate the pitfalls of the symmetric double-sided TWR (SDS-TWR) method, which is commonly used to reduce the TOF error due to clock drifts.