Ultra-wideband Localization System Research Articles

Static and Dynamic Evaluation of an UWB Localization System for Industrial Applications

Many applications in the context of Industry 4.0 require precise localization. However, indoor localization remains an open problem, especially in complex environments such as industrial environments. In recent years, we have seen the emergence of Ultra WideBand (UWB) localization systems. The aim of this article is to evaluate the performance of a UWB system to estimate the position of a person moving in an indoor environment. To do so, we implemented an experimental protocol to evaluate the accuracy of the UWB system both statically and dynamically. The UWB system is compared to a ground truth obtained by a motion capture system with a millimetric accuracy.

Iterative Bias Estimation for an Ultra-Wideband Localization System

An iterative bias estimation framework is presented that mitigates position-dependent ranging errors often present in ultra-wideband localization systems. State estimation and control are integrated, such that the positioning accuracy improves over iterations. The framework is experimentally evaluated on a quadcopter platform, resulting in improvements in the tracking performance with respect to ground truth, and also smoothing the overall flight by significantly reducing unwanted oscillations; see https://youtu.be/J-htfbzf40U for a video.

Edge Inference for UWB Ranging Error Correction Using Autoencoders

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.

An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems

Ultra-wideband technology has the merits of high temporal resolution and stability, and it has been widely used for high-accuracy localization and tracking. However, most ultra-wideband localization systems need multiple anchors for trilateration, which results in high system cost, large messages overhead, and insufficient extraction of information. In this paper, we propose a single-anchor localization (SAL) mehtod that achieves high-accuracy multi-agent localization with high efficiency. In the proposed method, the anchor broadcasts the first two messages and then each agent responds one message to the anchor (quasi-)simultaneously. Based on the received message with superpositioned agent responses, the time-of-flight and angle-of-arrival information from all agents to the anchor can be extracted altogether. We implement the localization system in two indoor environments, and show that the proposed method can achieve decimeter-level accuracy for multiple agents using three messages. Our method provides design guidelines for high-accuracy and high-efficiency multi-agent localization systems.

Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy

Over the years, several Ultrawideband (UWB) localization systems have been proposed and evaluated for accurate estimation of the position for pedestrians. However, most of them are evaluated for a particular wearable sensor position; hence, the accuracy obtained is subject to a given wearable sensor position. This paper is focused on studying the effects of body wearable sensor positions i.e., chest, arm, ankle, wrist, thigh, forehead, and hand, on the localization accuracy. According to our results, the forehead and the chest provide the best and worst body sensor location for tracking a pedestrian, respectively. With the wearable sensor at the forehead and chest position, errors lower than 0.35 m (90th percentile) and 4 m can be obtained, respectively. The reason for such a contrast in the performance lies in the fact that, in non-line-of-sight (NLOS) situations, the chest generates the highest multipath of any part of the human body. Thus, the large errors obtained arise due to the signal arriving at the target wearable sensor by multiple reflections from interacting objects in the environment rather than by direct line-of-sight (LOS) or creeping wave propagation mechanism.

Static and Dynamic Evaluation of an UWB Localization System for Industrial Applications

Many applications in the context of Industry 4.0 require precise localization. However, indoor localization remains an open problem, especially in complex environments such as industrial environments. In recent years, we have seen the emergence of Ultra WideBand (UWB) localization systems. The aim of this article is to evaluate the performance of a UWB system to estimate the position of a person moving in an indoor environment. To do so, we implemented an experimental protocol to evaluate the accuracy of the UWB system both statically and dynamically. The UWB system is compared to a ground truth obtained by a motion capture system with a millimetric accuracy.

RMLNet-A Reliable Wireless Network for a Multiarea TDOA-Based Localization System.

Ultrawideband (UWB) wireless communication is a promising spread-spectrum technology for accurate localization among devices characterized by a low transmission power, a high rate and immunity to multipath propagation. The accurately of the clock synchronization algorithm and the time-difference-of-arrival (TDOA) localization algorithm provide precise position information of mobile nodes with centimeter-level accuracy for the UWB localization system. However, the reliability of target node localization for multi-area localization remains a subject of research. Especially for dynamic and harsh indoor environments, an effective scheme among competing target nodes for localization due to the scarcity of radio resources remains a challenge. In this paper, we present RMLNet, an approach focus on the medium access control (MAC) layer, which guarantees general localization application reliability on multi-area localization. Specifically, the design requires specific and optimized solutions for managing and coordinating multiple anchor nodes. In addition, an approach for target area determination is proposed, which can approximately determine the region of the target node by the received signal strength indication (RSSI), to support RMLNet. Furthermore, we implement the system to estimate the localization of the target node and evaluate its performance in practice. Experiments and simulations show that RMLNet can achieve localization application reliability multi-area localization with a better localization performance of competing target nodes.

An accurate UWB based localization system using modified leading edge detection algorithm

Ultra-wideband (UWB) localization systems have gained wide attention in precise indoor positioning applications, due to the usage of high bandwidth signals. We propose a modified leading edge detection algorithm for estimating the time of arrival (TOA) of the received UWB signal. The proposed method performs well even under low signal-to-noise ratio (SNR) conditions. We simulate a UWB localization system using the proposed algorithm and evaluate its performance under additive white Gaussian noise (AWGN). We study the influence of tuning parameters of the proposed algorithm on the positioning accuracy. We evaluate 1D and 2D positioning accuracies of the localization system using the optimal tuning parameters of the proposed algorithm. We further evaluate the performance of the proposed algorithm in multipath conditions. Simulation results show that the proposed algorithm provides enhanced positioning accuracy compared to the previously published methods including the state-of-the-art methods. We improve the positioning accuracy of the localization system further by deploying more base stations.

TDOA estimation using a pair of synchronized anchor nodes

AbstractNowadays, as indoor localization is getting more popular, there is a growing need for reliable and accurate techniques of position determination. Recently, ultrawideband (UWB)-based systems are gaining popularity, since they make achieving positioning errors in the range of dozens of centimeters or even single centimeters, possible. The Time Difference of Arrival (TDOA)-based systems are especially attractive because they allow to simplify tags, in which functionality can be limited to transmission of packets. However, one of TDOA-based solution drawbacks is a need for strict synchronization between anchor nodes, which may be hard to provide in indoor environment. In the paper, a novel method for simplifying synchronization in TDOA-based UWB localization system is described. The paper presents two system architectures based on pairs of synchronized nodes. Results of simulations and experiments included in the article allow for evaluation of both solutions.

Non-Line-of-Sight Identification Based on Unsupervised Machine Learning in Ultra Wideband Systems

Identification of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation conditions is very useful in ultra wideband localization systems. In the identification, supervised machine learning is often used, but it requires exorbitant efforts to maintain and label the LOS and NLOS database. In this paper, we apply unsupervised machine learning approach called “expectation maximization for Gaussian mixture models” to classify LOS and NLOS components. The key advantage of applying unsupervised machine learning is that it does not require any rigorous and explicit labeling of the database at a certain location. The simulation results demonstrate that by using the proposed algorithm, LOS and NLOS signals can be classified with 86.50% correct rate, 12.70% false negative, and 0.8% false positive rate. We also compare the proposed algorithm with the existing cutting-edge supervised machine learning algorithms in terms of computational complexity and signals' classification performance.

DeepTAL: Deep Learning for TDOA-Based Asynchronous Localization Security With Measurement Error and Missing Data

The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements are not yet complete. Like any other process, localization also has security requirements. The use of ultra-wideband (UWB) indoor localization systems have recently grown quickly in industries, with a reliable, fast, and have high accuracy performances. In particular, time difference-of-arrival (TDOA) is one of the widely used localization models. However, as TDOA measurement errors increase, the accuracy of the localization decreases. The accuracy of the TDOA measurement is influenced the accuracy of the localization and affected by multiple factors such as time synchronization, errors in sensor positions, missing data is caused by network attack. To reduce the influence of a sensor measurement error in localization, this paper proposes an improved localization algorithm for source localization using deep learning to address a TDOA measurements error or missing data in an asynchronous localization called DeepTAL. Unlike the conventional algorithm, DeepTAL can obtain highly accurate localization data in the presence of TDOA measurement errors or missing data. The algorithm starts with TDOA measurements without time synchronization. A network based on Long Short-Term Memory (LSTM) is then applied to achieve a stronger learning and better representation of the determined target state and TDOA prediction. The network can express features more abstractly at higher levels and increase recognition accuracy. After that, the target node is accurately located by TDOAs. We implement the DeepTAL algorithm on an asynchronous localization system with UWB signals. The experiments show that the proposed DeepTAL algorithm is efficient in improving the localization precision as measurement errors or missing data.

A Novel NLOS Mitigation Algorithm for UWB Localization in Harsh Indoor Environments

Non-line-of-sight (NLOS) propagation of radio signals can significantly degrade the performance of ultra-wideband localization systems indoors, it is hence crucial to mitigate the NLOS effect to enhance the accuracy of positioning. The existing NLOS mitigation algorithms to improve localization accuracy are either by compensating range errors through NLOS identification and mitigation methods for ranging or by using dedicated localization techniques. However, they are only applicable to some specific scenarios due to some special assumptions or the need of a priori knowledge, such as thresholds and distribution functions. Another disadvantage is that they neither have the capability to evaluate the magnitude of NLOS effect nor take account of the residual NLOS range errors during location estimation. To remedy these problems, this paper proposes a less environment-dependent and a priori knowledge-independent NLOS identification and mitigation method for ranging which is able to determine the specific NLOS channel. Based on the identified channel information, a rule is developed to select appropriate NLOS ranges for location estimation. Meanwhile, an equality constrained Taylor series robust least squares (ECTSRLS) technique is proposed to suppress residual NLOS range errors by introducing robustness to Taylor series least squares method. All these constitute our FCE-ECTSRLS NLOS mitigation algorithm. The performance of the proposed algorithm is compared with four existing NLOS mitigation algorithms by both static and mobile localization experiments in a harsh indoor environment. Experimental results have demonstrated that the proposed FCE-ECTSRLS algorithm outperforms the other four algorithms significantly.

Calibrating Away Inaccuracies in Ultra Wideband Range Measurements: A Maximum Likelihood Approach

A calibration framework for an ultra-wideband localization system employing inertial measurement units is presented. No external motion capture system or other sensors are required for the calibration procedure. Given a covariance function for the error in the range measurements, a range measurement model based on a Gaussian process is obtained by maximizing the joint likelihood of angular rate, acceleration, and range measurements. The framework is experimentally evaluated, and it is shown how the resulting measurement model, integrated in a standard Kalman filter, can be used for real-time localization on a platform with limited computational resources. The calibration significantly improves the localization accuracy for randomly generated trajectories and different localization system setups.

Improving the Robustness of Control-Grade Ultra-Wideband Localization

Ultra-wideband based localization technologies gained more and more attention over the recent years. Most of the predominant research focusses on two-way ranging based system topologies that feature limited multi-user scalability. This work aims to improve the accuracy and robustness of a highly scalable control-grade time-difference of arrival based localization system. The specific goal addressed in this work is to improve the robustness under non-line-of-sight conditions using ultra-wideband specific signal quality assessment and inertial sensor fusion. The accuracy of the proposed method is experimentally analyzed in two experiments. One experiment evaluates the performance under best-case conditions. A second experiment introduces strong interference through moving assets. Here, an accuracy improvement of over 60 % could be achieved compared to previous results. In order to relate those results, an experimental comparison to a widely used angle of arrival capable state of the art ultra-wideband localization system is made. It could be shown that the proposed method is more accurate than the state of the art in the strong interference scenario.

A Novel Biomechanical Model-Aided IMU/UWB Fusion for Magnetometer-Free Lower Body Motion Capture

The available human body inertial motion capture (MoCap) systems are aided by magnetometers to remove the drift error in yaw angle estimation, which in turn limits their application in the presence of long-lasting magnetic disturbances in indoor environments. This paper introduces a magnetometer-free algorithm for lower-body MoCap including 3-D localization and posture tracking by fusing inertial sensors with an ultrawideband (UWB) localization system and a biomechanical model of the human lower body. Using our novel Kalman filter-based fusion algorithm, the UWB localization data aided by the biomechanical model can eliminate the drift in inertial yaw angle estimation of the lower-body segments. This magnetometer-free yaw angle estimation makes the algorithm insensitive to the magnetic disturbances. The algorithm is benchmarked against the optical MoCap system for various indoor activities including walking, jogging, jumping, and stairs ascending/descending. The results show that the proposed algorithm outperforms the available magnetometer-aided algorithms in yaw angle tracking under magnetic disturbances. In a uniform magnetic field, the algorithm shows similar accuracies in localization and joint angle tracking when compared with the magnetometer-aided methods. The localization accuracy of the proposed method is better than 4.5 cm in a 3-D space and its accuracy for knee angle tracking is about 3.5° and 4.5° in low and high dynamic motions, respectively.

High-Accuracy Tracking Using Ultrawideband Signals for Enhanced Safety of Cyclists

In this paper, an ultrawideband localization system to improve the cyclists’ safety is presented. The architectural solutions proposed consist of tags placed on bikes, whose positions have to be estimated, and anchors, acting as reference nodes, located at intersections and/or on vehicles. The peculiarities of the localization system in terms of accuracy and cost enable its adoption with enhanced risk assessment units situated on the infrastructure/vehicle, depending on the architecture chosen, as well as real-time warning to the road users. Experimental results reveal that the localization error, in both static and dynamic conditions, is below 50 cm in most of the cases.

Development and experimental evaluation of a 3D ultra-wideband localization system

Development and experimental evaluation of a 3D ultra-wideband localization system

A Gaussian mixture Bernoulli filter for extended target tracking with application to an ultra-wideband localization system

This paper presents a Gaussian mixture (GM) implementation of the Bernoulli filter for extended target tracking, which we call the extended target GM Bernoulli (ET-GM-Ber) filter. Closed form expressions for the ET-GM-Ber filter recursions are obtained. A clustering step is integrated into the measurement update stage in order to have a computationally tractable filter. Performance of the proposed filter is tested both on the simulated data and experimental data collected using an ultra-wideband (UWB) localization system. Simulations and experimental results demonstrate the accurate and effective performance of the proposed filter.

UWB-Aided Inertial Motion Capture for Lower Body 3-D Dynamic Activity and Trajectory Tracking

This paper introduces a novel method for simultaneous 3-D trajectory tracking and lower body motion capture (MoCap) under various dynamic activities such as walking and jumping. The proposed method uses wearable inertial sensors fused with an ultrawideband localization system using a cascaded Kalman filter-based sensor fusion algorithm, which consists of a separate orientation filter cascaded with a position/velocity filter. In addition, to further improve the joint angle tracking accuracy, anatomical constraints are applied to the knee joint. Currently, available self-contained inertial tracking methods are not only drift-prone over time but also their accuracy is degraded under fast movements with unstable ground contact states due to the lack of external references. However, our experimental results, which benchmark the system against a reference camera-based motion tracking system, show that the proposed fusion method can accurately capture the dynamic activities of a subject without drift. The results show that the proposed system can maintain similar accuracies between fast and slow motions in lower body MoCap and 3-D trajectory tracking. The obtained accuracies of the system for 3-D body localization and knee joint angle tracking for fast motions were less than 5 cm and 2.1°, respectively.

A magnetometer-free indoor human localization based on loosely coupled IMU/UWB fusion.

The magnetic distortions in indoor environment affects the accuracy of yaw angle estimation using magnetometer. Thus, the accuracy of indoor localization based on inertial-magnetic sensors will be affected as well. To address this issue, this paper proposes a magnetometer-free solution for indoor human localization and yaw angle estimation. The proposed algorithm fuses a wearable inertial sensor consisting of MEMS-based accelerometer and gyroscope with a portable ultra-wideband (UWB) localization system in a cascaded two-step filter consisting of a tilt Kalman filter and a localization Kalman filter. By benchmarking against an optical motion capture system, the experimental results show that the proposed algorithm can accurately track position and velocity as well as the yaw angle without using magnetometer.