Window-Based Channel Impulse Response Prediction for Time-Varying Ultra-Wideband Channels.

The error compensation and suppression effects of traditional ultrawideband (UWB) ranging in non Line of Sight (NLOS) environments are limited. The contribution of specific occlusions to UWB spatial perception in NLOS is ignored. To achieve comprehensive sensing of spatial information by UWB, we initially analyze the channel impulse response (CIR) and the underlying parameters of registers during UWB communication. By comparing the difference between NLOS and line-of-sight (LOS) environments for each parameter on a continuous time series, a fast discriminative method for UWB environment conversion is proposed. Further, combining the ensemble learning XGBoost classifier, an efficient NLOS occlusion recognition method is proposed. At the same time, an algorithm optimization based on a discrete degree threshold is designed. It is based on loss function probability matrix-weighted predictive labeling. The prediction matrix of the loss function of the XGBoost algorithm is used as label weights. The UWB prediction labels of continuous time series are weighted, which mitigates the effect of low-probability data on the overall prediction results. Finally, UWB spatial sensing experiments are carried out to verify the reliability of the proposed method. The experimental results show that the mutation in the parameter profile can effectively perceive the LOS/NLOS transition. The recognition accuracy of the proposed occlusion recognition method in conditions of human, metal, and wall occlusion is 94.44%, 92.00%, and 95.87%, respectively. In contrast to the origin method, the suggested algorithm’s average recognition accuracy has increased by 16.71%. Its precise recognition accuracy makes UWB spatial sensing more effective.

At present, people spend most of their time indoors, so it is necessary to study high-precision positioning. Ultra wide band (UWB) can obtain the ranging accuracy with centimeter-level error. However, since the indoor environment is more complex than the outdoor environment, positioning errors tend to be generated during the UWB positioning due to the influence of non-line of sight (NLOS). Therefore, this paper investigates how to identify the NLOS environment and reduce the NLOS error. This paper proposes a method to determine the line of sight (LOS) environment credibility and uses it to determine the NLOS environment. The data with NLOS error is determined according to the standard deviation of the residual between the square of the predicted ranging value and the square of the measured ranging value. Then the data with NLOS error is compensated by complementary filtering. Finally, the compensated measurement data and the optimal estimation of the state at the previous moment are imported into the Kalman filter (KF) to determine the optimal estimation at the current moment.

With the booming demand of indoor position information, Wireless signals (Wi-Fi, Bluetooth, Ultra-Wide-Band etc.) are investigated to construct Indoor Positioning System (IPS). Among these wireless signals, UWB (Ultra-Wide-Band) is recognized as the most promising technology to construct IPS with decimeter-level positioning accuracy. However, there are various objects in indoor environments, UWB signals might be reflected by these surrounding objects. These None-Line-Of-Sight (NLOS) signals will induce additional errors to the distance measurements between the anchor and agent. Therefore, NLOS/LOS (Line-Of-Sight) signals classification should be carried out for identifying the NLOS reception. In UWB based IPS, the distance information is extracted through the Channel Impulse Response (CIR) waveforms. NLOS reception will lead to different CIR waveforms compared with that from the LOS signal, this difference could be characterized for NLOS/LOS signals classification. However, the hardware circuits, signal transmitting path and the NLOS reception will bring about noises to the CIR waveforms, which might influence the signal type identification. Motivated by this problem, in this letter, a reversible transformation method was proposed for de-nosing the CIR data, and a Conventional Neural Network (CNN) was employed to identify the NLOS signal. Raw and de-noised CIR dataset were both input to the CNN for assessing the effect of the de-noising method. With dataset collected from seven different sites, the results showed that the reversible transformation method was able to increase the Signal Noise Ratio (SNR) values of these CIR datasets. After the de-noising, the CNN performed higher classification accuracy with less convolutional layers. Moreover, the de-noising operation brought about higher NLOS signal identification accuracy increase than that of LOS signals.

This paper considers the global localization of a pedestrian via an ultra-wideband (UWB) ranging aided inertial navigation system (INS) and aims to address the challenges involved in proper processing of UWB range measurements. Even though UWB offers a decimeter level accuracy in line-of-sight (LoS) ranging, its accuracy degrades significantly in non-line-of-sight (NLoS). This drop in accuracy is due to a significant unknown positive bias in the NLOS range measurements. Therefore, the measurement models used in UWB LoS and NLoS ranging conditions are different, and proper processing of NLoS measurements requires a bias compensation measure. Previous work on bias compensation for UWB ranging that is used to aid an INS based localization assumes that the LoS and NLoS measurements are identified and distinguished from each other with absolute certainty. However, in practice, this assumption is hard to satisfy, and identifiers that determine the type of UWB range measurements deliver their results with only some level of certainty. To take into account the probabilistic nature of the NLoS identifiers, in this paper, we propose an adaptive localization based on the first-order generalized pseudo Bayesian (GPB) method to seamlessly handle the measurement model switching between LoS and NLoS UWB range measurements. The effectiveness of our proposed method is demonstrated via an experiment for pedestrian geolocation using a shoe-mounted INS system aided by UWB range measurements with respect to beacons with known locations.

In Ultra-Wide Band (UWB) positioning, wireless signals are subject to non-line-of-sight (NLOS) propagation due to obstruction by obstacles, which leads to ranging and positioning estimation errors. How to accurately and efficiently identify line-of-sight (LOS) and NLOS propagation paths is a key research task in UWB positioning systems. By effectively integrating the characteristics of global channel impulse response (CIR) sequence features and statistical time-domain features, a dual-branch feature fusion Transformer (DBFF-Transformer) is proposed for NLOS path identification. Firstly, the original CIR sequence data is processed using the Transformer to learn the global feature relationships within the data. Secondly, four key time-domain features are extracted from the CIR sequence: the first-path energy ratio, the root-mean-square time delay spread, the kurtosis and the phase difference. Finally, by integrating the sequence features and the time-domain features, the two features’ branches are fused through a fully connected network. The proposed method is evaluated in two typical indoor scenarios from the latest open-source datasets of the eWINE project. The ablation experiment proves that the fusion of the sequence features and time-domain features of the CIR sequence can effectively improve NLOS identification accuracy. The identification accuracy in the two experimental scenarios is 95.9% and 95.7%, with F1 scores of 97.2% and 97.1% and Recall of 97.4% and 96.4%, respectively. The comparative analysis of the DBFF-Transformer with the state-of-the-art baseline models demonstrates superior accuracy and robustness, which can provide a novel solution for NLOS identification in UWB indoor positioning.

In the rapidly evolving Internet of Things (IoT) landscape, accurate indoor positioning is increasingly vital. The proposed algorithm synergizes an ultrawideband (UWB) sensor with an inertial measurement unit (IMU) and artificial intelligence to obtain precise positioning in nonline-of-sight (NLOS) scenarios. In the proposed UWB module, a large language model (LLM) such as bidirectional encoder representations from transformers (BERT) algorithm is designed to utilize the channel impulse response (CIR) for effective NLOS identification and UWB ranging trustworthiness evaluation. Concurrently, the IMU module is also designed with BERT to recognize various pedestrian activity states, thereby optimizing positioning. BERT’s self-attention mechanism and deep learning (DL) bidirectional training efficiently extract essential features from sequential data, capturing both local and global information. The integration of both UWB and IMU through a proposed tightly coupled algorithm significantly boosts positioning performance. Experimental campaigns demonstrate an average NLOS identification accuracy, line-of-sight (LOS), and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}2$ </tex-math></inline-formula> of 98.8%, 99.4%, and 0.9926, respectively. These performances surpass the state-of-the-art least-squares support vector machine (LS-SVM), convolutional neural network (CNN), and CNN with long short-term memory (CNN-LSTM) up to 17.66% in NLOS identification. In terms of pedestrian activity recognition using BERT, the BERT algorithm achieves a precision (recall) of 99.3% (99.4%), notably outperforming the CNN and CNN-LSTM by 17.9% (16.2%) and 11.9% (10.9%), respectively. Finally, the UWB-IMU algorithm significantly enhances positioning accuracy by 80.5%, outperforming Kalman, LSTM-EKF, and particle filter (PF) methods by 68.1%, 48.3%, and 45.0%, respectively. The proposed approach presents a robust solution for indoor positioning for IoT applications, particularly in challenging NLOS environments.

Accurate positioning in harsh environments can enable many application, such as search-and-rescue in emergency situations. For this problem, ultra-wideband (UWB) technology can provide the most accurate range estimates, which are required for range-based positioning. However, it still faces a problem in non-line-of-sight (NLOS) environments, in which range estimates based on time-of-arrival (TOA) are positively biased. There are many techniques that try to address this problem, mainly based on NLOS identification and NLOS error mitigation. However, these techniques do not exploit all available information from the UWB channel impulse response. In this paper, we propose a novel ranging technique based on kernel principal component analysis (kPCA), in which the selected channel parameters are projected onto nonlinear orthogonal high-dimensional space, and a subset of these projections is then used for ranging. We tested this technique using UWB measurements obtained in a basement tunnel of Linkoping university, and found that it provides much better ranging performance comparing with standard techniques based on PCA and TOA.

Ultrawideband (UWB) radio frequency positioning technology has found extensive applications in indoor and outdoor localization due to its strong anti-interference capabilities, high penetration power, and precise measurement accuracy. However, its positioning accuracy significantly decreases under nonline-of-sight (NLOS) conditions, particularly in dynamic NLOS environments. Therefore, it is essential to identify NLOS propagation and mitigate its associated errors. To overcome these issues, this study proposes a novel enhanced domain-adversarial neural network for UWB signal occlusion recognition (EDANN-SOR). EDANN-SOR integrates hybrid manually extracted channel impulse response (CIR) features, effectively aligning the source and target domains and extracting discriminative and domain-invariant UWB CIR features in dynamic NLOS environments. Through adversarial learning, our method achieves high generalization with minimal CIR sample requirements, reducing the need for extensive data collection and significantly enhancing robustness in distance measurement and positioning under dynamic NLOS conditions. In contrast to state-of-the-art NLOS identification and transfer learning (TL) techniques, our method excels in both binary and multiclass NLOS classification. Specifically, it achieves an accuracy of over 97.36% in conventional LOS/NLOS binary classification across scenarios and 97.07% in multiclass classification. The integration of manually extracted features has been proved effective in improving the EDANN-SOR model’s ability to distinguish among LOS, human, and glass obstacles.

Abstract. Ultra-wideband (UWB) technology stands out among numerous indoor positioning techniques due to its high operating frequency, low interception capability, resistance to multipath effects, and strong penetration. The UWB uses the time-of-arrival (TOA) to estimate the distance between the transmitter and receiver anchors in centimeter accuracy. However, in complex indoor positioning environments, obstacles such as walls, glass windows, metal plates, and wooden doors may block and reflect signals, inevitably causing non-line-of-sight (NLOS) errors that significantly affect positioning accuracy. The NLOS signal has lower signal energy due to the reflections. Thus, the channel impulse responses (CIR) from NLOS and LOS are different. To address the NLOS signal identification issue in UWB positioning, we utilize UWB CIR data collected from various positioning scenarios as the data source. CIR waveform input features are provided for the NLOS signal recognition model, and four machine learning models—Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), K-Nearest Neighbors (KNN), and XGBoost—are trained and optimized for NLOS signal recognition. The aim of the study is to analyze the performance of different machine learning algorithms for NLOS signal recognition in UWB indoor localization using these features. Experimental results indicate that machine learning-based NLOS signal recognition algorithms can achieve an accuracy of approximately 77.46%, precision of 80.46%, and an F1 score of 0.81. Among the four models, the XGBoost model demonstrates generally better recognition performance.

Ultra-wideband (UWB) and WiFi technologies have been widely proposed for the implementation of accurate and scalable indoor positioning systems (IPSs). Among different approaches, fingerprinting appears particularly suitable for WiFi IPSs and was also proposed for UWB IPSs, in order to cope with the decrease in accuracy of time of arrival (ToA)-based lateration schemes in the case of severe multipath and non-line-of-sight (NLoS) environments. However, so far, the two technologies have been analyzed under very different assumptions, and no fair performance comparison has been carried out. This paper fills this gap by comparing UWB- and WiFi-based fingerprinting under similar settings and scenarios by computer simulations. Two different k-nearest neighbor (kNN) algorithms are considered in the comparison: a traditional fixed k algorithm, and a novel dynamic k algorithm capable of operating on fingerprints composed of multiple location-dependent features extracted from the channel impulse response (CIR), typically made available by UWB hardware. The results show that UWB and WiFi technologies lead to a similar accuracy when a traditional algorithm using a single feature is adopted; when used in combination with the proposed dynamic k algorithm operating on channel energy and delay spread, UWB outperforms WiFi, providing higher accuracy and more degrees of freedom in the design of the system architecture.

The radio channel modeling and location system are the focus of many research fields. However, their performance is largely limited by line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Currently, the most popular LOS/NLOS identification algorithm is based on the channel impulse response (CIR) feature. The decision threshold is applied to identify between LOS and NLOS conditions on this basis. However, the features around the threshold are usually very similar, which leads to low LOS/NLOS accuracy. In this letter, the feature extracted from the CIR is used to fit its distribution with the Gaussian mixture function. Then, a soft decision algorithm is used to classify the signal. In the experimental simulations, the algorithm is tested according to the measured data. The results reveal that by choosing different feature combinations, the algorithm effectively improves the accuracy of LOS and NLOS identification.

The indoor localization and tracking of objects and humans with high accuracy is becoming increasingly important in a number of applications including healthcare, ambient assistant-living, surveillance, among others. Since Ultra-wideband (UWB) systems have a large operating bandwidth, they can provide centimeter (cm) level localization accuracy in Line-of-Sight (LoS) scenarios. However, current commercial UWB systems require the target to carry an active device (tag) so that it can be precisely located and this may be impractical in applications such as security and surveillance. In this work, we process experimental data obtained from a multi-static UWB radar network for the passive indoor localization of a moving target. Current fingerprinting-based passive localization techniques require a substantial radio-map survey in the offline training phase and labor-intensive fingerprint updates when there are changes in the environment. In this work, on the other hand, we propose to use the fine-grained physical layer information, known as Channel Impulse response (CIR), that is exchanged between UWB modules and capitalize on the fact that a moving person induces variations in the CIR that stand out against the background signal. Our results show that a walking target can be passively located with a median distance error as low as 0.55m in an indoor environment.

The low sound speed in water makes propagation delay (PD)-based range estimation attractive for underwater acoustic localization (UWAL). However, due to the long channel impulse response and the existence of reflectors, PD-based UWAL suffers from significant degradation when PD measurements of nonline-of-sight (NLOS) communication links are falsely identified as line-of-sight (LOS). In this paper, we utilize expected variation of PD measurements due to mobility of nodes and present an algorithm to classify the former into LOS and NLOS links. First, by comparing signal strength-based and PD-based range measurements, we identify object-related NLOS (ONLOS) links, where signals are reflected from objects with high reflection loss, for example, ships hull, docks, rocks and so on. In the second step, excluding PD measurements related to ONLOS links, we use a constrained expectation-maximization algorithm to classify PD measurements into two classes: LOS and sea-related NLOS (SNLOS), and to estimate the statistical parameters of each class. Since our classifier relies on models for the underwater acoustic channel, which are often simplified, alongside simulation results, we validate the performance of our classifier based on measurements from three sea trials. Both our simulation and sea trial results demonstrate a high detection rate of ONLOS links, and accurate classification of PD measurements into LOS and SNLOS.

Localization of people that do not carry active tags is needed in security as well as in rescue applications. Ultra-wideband (UWB) technology is promising due to its high ranging resolution capability, robustness against multipath interference and obstacle penetration among others. In this chapter, an approach for detection, localization and tracking of people using either a single UWB sensor or a distributed network of UWB sensor nodes is described. The background behind UWB sensing, a description of UWB sensor nodes and a concept for a distributed sensor network is presented. The basic principle of person detection based on the changes the person induces on the channel impulse response is explained. Two approaches for localization based on range-only observations that can be applied in singlesensor or multiple-sensor scenarios or in the presence of a single person or multiple people are presented. The concept and each step of the approach are illustrated using data obtained in a measurement campaign at TU Ilmenau from an office environment.

Ultra-Wide-Band (UWB) ranging sensors have been widely adopted for robotic navigation thanks to their extremely high bandwidth and hence high resolution. However, off-the-shelf devices may output ranges with significant errors in cluttered, severe non-line-of-sight (NLOS) environments. Recently, neural networks have been actively studied to improve the ranging accuracy of UWB sensors using the channel-impulse-response (CIR) as input. However, previous works have not systematically evaluated the efficacy of various packet types and their possible combinations in a two-way-ranging transaction, including poll, response and final packets. In this paper, we firstly investigate the utility of different packet types and their combinations when used as input for a neural network. Secondly, we propose two novel data-driven approaches, namely FMCIR and WMCIR, that leverage two-sided CIRs for efficient UWB error mitigation. Our approaches outperform state-of-the-art by a significant margin, further reducing range errors up to 45%. Finally, we create and release a dataset of transaction-level synchronized CIRs (each sample consists of the CIR of the poll, response and final packets), which will enable further studies in this area.