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. Index Terms-time of flight (TOF), two way ranging (TWR), analytical model, TOF error model, time of arrival (TOA), time based ranging techniques, indoor positioning

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.

In the past, several contributions and proposals for the implementation of UWB-based localization and positioning solutions on the system level were made. However, most of them are limited to a uni-directional approach, i.e. data communication is in one direction (from a transmitter to a receiver). This restricts the systems use-case to either navigation or tracking. In this paper, we introduce a bidirectional UWB-based localization system which is capable of acting as both a navigation and tracking system in a single platform. Moreover, we proposed a true-range multilateration method, that is solely based on the Cartesian equation of a sphere, as a positioning algorithm. Concerning this, we also present a non-line-of-sight mitigation technique in the paper. The experimental evaluation of the proposed system was done compared to the commercially available UWB system. In the experiments, we used a Vicon camera system as a reference.

In this paper, we analyze five true-range positioning algorithms for UWB-based localization systems. The evaluated algorithms are: (i) trilateration using a geometric method, (ii) a closed-form multilateration solution using least squares, (iii) an iterative approach using first-order Taylor series, a recursive solution based on (iv) the Extended Kalman Filter (EKF), and (v) the Unscented Kalman Filter (UKF). In contrast to the existing comparative studies in literature, which are solely based on simulation results, our analysis is based on experimental evaluations. The evaluated algorithms are strictly chosen for a scenario, where a true-range multilateration method is applicable. True-range means the accuracy of the measured ranges is not influenced by the clock drift errors. The performance comparison of the five algorithms is examined and discussed in the paper.