Adaptive Not-direct-path Identification in UWB Localization

UWB (Ultra-wideband) has been shown to be a promising technology to provide accurate positioning for the Internet of Things. However, its performance significantly degrades in practice due to Non-Line-Of-Sight (NLOS) issues. Various approaches have implicitly or explicitly explored the problem. In this article, we propose RefLoc , which leverages the unique benefits of UWB to address the NLOS problem. While we find that NLOS links can vary significantly in the same environment, LOS links possess similar features that can be captured by the high bandwidth of UWB. Specifically, the high-level idea of RefLoc is to first identify links among anchors with known positions and leverage those links as references for tag link identification. To achieve this, we address the practical challenges of deriving anchor link status, extracting qualified link features, and inferring tag links with anchor links. We implement RefLoc on commercial hardware and conduct extensive experiments in different environments. The evaluation results show that RefLoc achieves an average NLOS identification accuracy of 96% in various environments, improving the state-of-the-art by 10%, and reduces 80% localization error with little overhead.

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.

Recent advances in the field of ultra wide band (UWB) localization system has brought renewed interest to the problem of indoor localization system, which has a wide range of applications, such as localization in mine, commodity localization in market or factory and other fields. However, in real scenario, the time synchronization and the limited located users problems are widely existed. An indoor localization system based on UWB is proposed in this paper, where a pseudo time synchronization algorithm is proposed to solve time synchronization problem. Moreover, a signal communication mechanism is established, by which located users only receive signals transmitted from reference base stations. In this case, it can locate unlimited number of users simultaneously in theory. The system has high localization accuracy and stability, and achieves the indoor 3D localization, which can be seen from the indoor static localization and the indoor dynamic localization experiments, respectively.

In the post-epidemic era, with the resumption of production, orders for large-scale and small-batch production exponentially increase, new challenges to the productivity of the traditional production line have appeared, such as the redundant information, complex production process, which lead the traditional monitoring system cannot satisfy the requirements of rapid information interaction between management and executive layer in the workshop. According to the healthy control during epidemic, physical contact between workers should be reduced. Therefore, Ultra Wide Band(UWB) indoor localization and Augmented Reality(AR) technology are introduced, based on localization information and mobile device camera attitude estimation, an "actively push" mobile monitoring system can be built, which makes the workshop managers timely and completely monitor the manufacturing resources, non-contact production was also ensured. Finally, taking one equipment in a workshop as an example, the virtual visualization models are constructed in Unity software, and the effectiveness of the mobile monitoring system is realized based on UWB indoor localization technology and AR technology.

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.

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.

Indoor localization knows many applications, such as industry 4.0, warehouses, healthcare, drones, etc., where high accuracy becomes more critical than ever. Recent advances in ultra-wideband localization systems allow high accuracies for multiple active users in line-of-sight environments, while they still introduce errors above 300 mm in non-line-of-sight environments due to multi-path effects. Current work tries to improve the localization accuracy of ultra-wideband through offline error correction approaches using popular machine learning techniques. However, these techniques are still limited to simple environments with few multi-path effects and focus on offline correction. With the upcoming demand for high accuracy and low latency indoor localization systems, there is a need to deploy (online) efficient error correction techniques with fast response times in dynamic and complex environments. To address this, we propose (i) a novel semi-supervised autoencoder-based machine learning approach for improving ranging accuracy of ultra-wideband localization beyond the limitations of current improvements while aiming for performance improvements and a small memory footprint and (ii) an edge inference architecture for online UWB ranging error correction. As such, this paper allows the design of accurate localization systems by using machine learning for low-cost edge devices. Compared to a deep neural network (as state-of-the-art, with a baseline error of 75 mm) the proposed autoencoder achieves a 29% higher accuracy. The proposed approach leverages robust and accurate ultra-wideband localization, which reduces the errors from 214 mm without correction to 58 mm with correction. Validation of edge inference using the proposed autoencoder on a NVIDIA Jetson Nano demonstrates significant uplink bandwidth savings and allows up to 20 rapidly ranging anchors per edge GPU.

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.

In this paper, we propose a new joint time-of-arrival (TOA) estimation and non-line-of-sight (NLOS) identification approach for ultra-wideband (UWB) localization systems. We use an energy-based TOA estimation (EBE) algorithm. The NLOS detection is based on the ratio between the first path power (FPP) and the total signal power (TSP) of the received signal folded version (RSFV) of the EBE algorithm. The proposed approach is evaluated by Monte Carlo simulation on the CM1 and CM2 channel models of the standard IEEE802.15.4a. The simulation demonstrates that the correct identification of channel models with the proposed approach gives better results than those based on joint likelihood ratio.

With the rise of the Internet of Things and smart cities, indoor positioning technology becomes more and more important. Among the existing network technologies, ultra-wide band (UWB) has the advantages of high transmission rate, and strong penetration ability. However, its signal propagation is susceptible to N on- Line-of-Sight (NLOS) effects. Therefore, this paper focuses on indoor positioning in NLOS environments, and studies the Chan-Taylor co-weighted positioning algorithm improved by machine learning. The main research contents include: 1. A machine learning method is proposed to identify and classify NLOS signals. Using a sliding window to dynamically calculate the variance and mean of multiple groups of TDOA measurements in a short period of time as the input to the recognition algorithm, and classify them into three categories: Line-of-Sight (LOS), low NLOS error, and high NLOS error. 2. Based on the Chan- Taylor co-location algorithm, the algorithm introduces the concept of weighting coefficient, optimizes the observation data, and finally obtains the target position coordinates. The simulation results show that the positioning accuracy and system stability of the algorithm are improved compared with the Chan algorithm and the Chan-Taylor co-location algorithm.

Ultra-wideband (UWB) time of flight (ToF) ranging is nowadays one of the most attractive technologies to implement indoor localization solutions with reliable dm-level accuracy. UWB systems are generally resistant to multipath interference. However, non-line-of-sight (NLOS) components with small relative delays may introduce errors in the estimated distances well exceeding the typically achievable accuracy. We present an empirical accuracy assessment of UWB ranging using a commercial UWB system. A particular focus is on the magnitude and spatial patterns of multipath errors. A large dataset comprising distances between 0.2 and 100 m was collected in a geodetic metrology lab and outdoors for this purpose. We derived ground truth of the distances with superior accuracy using a laser tracker and a total station. The results show that multipath errors are highly repeatable if the geometrical configuration does not change, but can degrade the accuracy of the distance measurements locally by almost an order of magnitude. While typical biases were on the order of a few cm, peak values exceeding 60 cm were found, and the standard deviation, typically about 3 cm or less, increased up to almost 15 cm in certain locations. We have used a part of the large dataset to train an artificial neural network (ANN) for error prediction. The ANN uses the measured ranges and additional signal features, also provided by the ranging nodes, as inputs. For error mitigation, the ANN output was subtracted from the original range measurements. This improved the ranging accuracy over the whole test data by practically eliminating the bias on average and reducing the overall standard deviation by a factor of 3. The results indicate that the relation between the output of the UWB node and the range errors can be modeled to a practically useful degree even without in-depth knowledge of the specific UWB node’s measurement process and characteristics. The proposed approach can help to significantly improve the UWB ranging performance in presence of multipath and thus also the accuracy of a corresponding localization system.

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.

This paper describes a design effort in a smart tracking prototype, in an attempt to provide real-time, mobile and seamlessly indoor/outdoor location tracking for medical staff, patient or instruments in hospital environment, by integrating GPS (Global Position System) with UWB (Ultra Wideband). Ultra Wideband (UWB) technology can locate a person or asset to the accuracy of centimeters. While UWB technology is becoming popular for indoor position tracking, GPS technology has been around for some time for use in outdoor position tracking with an accuracy of 1 to 100 meters depending on the type of equipment used. It is envisaged that it is possible to combine GPS with UWB to give accurate position regardless of where the person or object is in indoor or outdoor. In view of the strong need for indoor-outdoor tracking in many environments such as hospitals, we looked into the possibility of integrating UWB and GPS to provide the target with seamless indoor-outdoor navigation capability and have developed a prototype. The system structure and experimental results will be illustrated in this paper. To our knowledge, no same system is reported yet.

Ultra-Wideband (UWB) communication system provides high accurate localization due to the high multi-path resolution capacity and large bandwidth of a UWB signal. Time Difference of Arrival(TDOA) technique provides very good accuracy, which is suitable for applying to UWB communication and localization systems. Estimating the target location from TDOA measurements is not an easy task since it is dealing with a set of nonlinear equations relating to the target location and the TDOA measurements. All kinds of method is by exhaustive search, which is computationally Intensive and suffers from the initialization and convergence problems. The paper develops an linear solution that estimate the target location, which does have convergence problems. Simulation results indicate that the proposed solution calculates the position of the target precisely.

Most ultrawideband (UWB) location systems already proposed for position estimation have only been individually evaluated for particular scenarios. For a fair performance comparison among different solutions, a common evaluation scenario would be desirable. In this paper, we compare three commercially available UWB systems (Ubisense, BeSpoon, and DecaWave) under the same experimental conditions, in order to do a critical performance analysis. We include the characterization of the quality of the estimated tag-to-sensor distances in an indoor industrial environment. This testing space includes areas under line-of-sight (LOS) and diverse non-LOS conditions caused by the reflection, propagation, and the diffraction of the UWB radio signals across different obstacles. The study also includes the analysis of the estimated azimuth and elevation angles for the Ubisense system, which is the only one that incorporates this feature using an array antenna at each sensor. Finally, we analyze the 3-D positioning estimation performance of the three UWB systems using a Bayesian filter implemented with a particle filter and a measurement model that takes into account bad range measurements and outliers. A final conclusion is drawn about which system performs better under these industrial conditions.