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.

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.

In this paper we present a respiration sensor suitable for an integration into a wireless body sensor worn around the chest. The thorax expansion and contraction during in- and exhalation is captured using a force-sensing resistor. Based on the captured thoracic movements, the breaths are determined with a peak detection algorithm. For evaluation, a treadmill experiment with five subjects was conducted using an ergospirometry system as a reference. Overall, an average deviation of -0.32±0.68 min ^-1 in the respiration rate between the ergospirometry and our sensor was observed. In general, the captured thoracic movements showed breaths as distinctive oscillations, but in some cases a non-optimal pressure transfer between thorax and sensor was observed. Therefore, a mechanical housing mechanism was developed. A comparison of our construction with a respiratory inductance plethysmography (RIP)-based sensor shows a close relationship with the captured thoracic movements during normal and deep respiration.

This work presents the novel multi-modal Variational Autoencoder approach $\mathbf{M}^{\mathbf{2}}\mathbf{VAE}$ which is derived from the complete marginal joint log-likelihood. This allows the end-to-end training of Bayesian information fusion on raw data for all subsets of a sensor setup. Furthermore, we introduce the concept of in-place fusion applicable to distributed sensing - where latent embeddings of observations need to be fused with new data. To facilitate in-place fusion even on raw data, we introduced the concept of a re-encoding loss that stabilizes the decoding and makes visualization of latent statistics possible. We also show that the $\mathbf{M}^{\mathbf{2}}\mathbf{VAE}$ finds a coherent latent embedding, such that a single nave Bayes classifier performs equally well on all permutations of a bi-modal Mixture-of-Gaussians signal. Finally, we show that our approach outperforms current VAE approaches on a bi-modal MNIST fashion-MNIST data set and works sufficiently well as a preprocessing on a tri-modal simulated camera LiDAR data set from the Gazebo simulator.