Ultra-wideband Localization Research Articles

X-law operation for energy-efficient time-of-arrival positioning to achieve higher accuracy with lower bandwidth: A game-changer for UWB localization devices

Ultra-wideband (UWB) technology is a good candidate for low-latency high-resolution localization. This paper proposes the x-law operation (XLO) to boost the positioning accuracy of UWB localization systems. The proposed scheme significantly reduces the contribution of low-SNR samples and increases the time resolution of the pulse. Thus, the XLO achieves a time-of-arrival (ToA) positioning accuracy that existing methods require more energy and bandwidth to attain. The significant performance gain of the XLO for localization makes the XLO a good candidate for simultaneous positioning and communication with low complexity, high performance, and low power consumption suited for Internet of Things (IoT) devices.

UWB indoor localization method based on neural network multi-classification for NLOS distance correction

It is well known that ultra-wideband (UWB) is widely used in building indoor positioning systems (IPS) because of its unique advantages. However, compared with the line-of-sight environment (LOS), UWB localization on none-line-of-sight (NLOS) channels has certain limitations, which will reduce the UWB ranging accuracy and location reliability in indoor environment. In this paper, a neural network (NN)-enhanced UWB positioning method is proposed. It can improve positioning performance by using the received channel impulse response (CIR) and UWB raw ranging data to classify the channel conditions and predict the distance. By training CNN-LSTM and MLP neural networks, the proposed method can alleviate the deterioration of localization performance caused by NLOS. The experimental results showed that the average NLOS recognition accuracy of five different obstacles including wooden doors, concrete walls, metal shelves, human body and glass windows reaches up to 92.36 %. In addition, the average root mean square error (RMSE) between the predicted distance and the true distance was 0.3123 m. The indoor positioning test was carried out by weighted least squares (WLS) and the average positioning error under three trajectories was 0.1223 m, which improved the performance by 83.56 % compared with the original UWB positioning system, thus proving its ability to reduce positioning degradation.

Strategy analysis of badminton players using deep learning from IMU and UWB wearables

Badminton is a fast-paced sport that requires a high level of skill and coordination. To improve their skills, players can use activity trackers to monitor different shots and activities. These trackers utilize inertial measurement units (IMUs), which measures acceleration and angular velocity on the rackets, and the ultra-wideband (UWB) sensors, which measure the location of the player. This study first analyzes the use of UWB localization for tracking badminton players on the court and analyzes the location where shots are played. Furthermore, this study focuses on using both IMU at the racket and wrist and UWB sensors to recognize strategies utilized in badminton matches, employing convolutional neural network (CNN) and Long-Short-Term Memory (LSTM) models. The goal is to classify thirteen badminton shots, as well as an extra class that contains non-shot activities. The output of this shot classifier is provided to the strategy recognition model, which can identify four main strategies, with eleven variations in total, alongside an additional class designated for non-strategy instances such as movement or rest intervals. We trained and tested the models on data from six skilled badminton players. The best results were achieved by using both IMU and UWB data. The proposed 2D-CNN achieved a shot classification accuracy of 90.9%, while the proposed LSTM achieved a strategy recognition accuracy of 80%. The results of this study suggest that neural networks can be used to effectively classify badminton shots and strategies to improve the training of badminton players, as well as to analyze match data.

Location-Aware Range-Error Correction for Improved UWB Localization.

In this paper, we present a novel localization scheme, location-aware ranging correction (LARC), to correct ranging estimates from ultra wideband (UWB) signals. Existing solutions to calculate ranging corrections rely solely on channel information features (e.g., signal energy, maximum amplitude, estimated range). We propose to incorporate a preliminary location estimate into a localization chain, such that location-based features can be calculated as inputs to a range-error prediction model. This way, we can add information to range-only measurements without relying on additional hardware such as an inertial measurement unit (IMU). This improves performance and reduces overfitting behavior. We demonstrate our LARC method using an open-access measurement dataset with distances up to 20 m, using a simple regression model that can run purely on the CPU in real-time. The inclusion of the proposed features for range-error mitigation decreases the ranging error 90th percentile (P90) by 58% to 15 cm (compared to the uncorrected range error), for an unseen trajectory. The 2D localization P90 error is improved by 21% to 18 cm. We show the robustness of our approach by comparing results to a changed environment, where metallic objects have been moved around the room. In this modified environment, we obtain a 56% better P90 ranging performance of 16 cm. The 2D localization P90 error improves as much as for the unchanged environment, by 17% to 18 cm, showing the robustness of our method. This method evolved from the first-ranking solution of the 2021 and 2022 International Conference on Indoor Position and Indoor Navigation (IPIN) Competition.

TDOA-based UWB indoor 1D localization via weighted sliding window filtering

Accurate localization of a person or object is a significant aspect in the field of industrial internet of things (IoT). Ultra-wideband (UWB) localization system, a promising technology, has been widely studied due to its high accuracy in 2D and 3D scenarios. Moreover, in narrow corridors and deep-well environments, the 1D localization is also an important application with the advantage of resource saving, however which attracts less attention. In this paper, we focus on the problem of 1D localization and propose a practical localization model with time difference of arrivals (TDOA). To solve the nonlinear 1D localization problem, we design an algorithm that integrates the dichotomous method and Newton's iterative method, which realizes an efficient solution. Meanwhile, in order to cope with the random errors in the real scenes, we introduce a sliding window algorithm to filter out the undesirable solutions. By observing the localization error map, we design a weighted sliding window algorithm to achieve further improvement of the localization accuracy. In addition, we derive the Cramér-Rao lower bound (CRLB) for 1D localization of TDOA. Simulations and practical experiments show that our proposed algorithm greatly benefits the localization accuracy of 1D TDOA and provides strong support for its practical application.

Exploiting Anchor Links for NLOS Combating 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.

Early detection of bovine respiratory disease in pre-weaned dairy calves using sensor based feeding, movement, and social behavioural data

Previous research shows that feeding and activity behaviours in combination with machine learning algorithms has the potential to predict the onset of bovine respiratory disease (BRD). This study used 229 novel and previously researched feeding, movement, and social behavioural features with machine learning classification algorithms to predict BRD events in pre-weaned calves. Data for 172 group housed calves were collected using automatic milk feeding machines and ultrawideband location sensors. Health assessments were carried out twice weekly using a modified Wisconsin scoring system and calves were classified as sick if they had a Wisconsin score of five or above and/or a rectal temperature of 39.5 °C or higher. A gradient boosting machine classification algorithm produced moderate to high performance: accuracy (0.773), precision (0.776), sensitivity (0.625), specificity (0.872), and F1-score (0.689). The most important 30 features were 40% feeding, 50% movement, and 10% social behavioural features. Movement behaviours, specifically the distance walked per day, were most important for model prediction, whereas feeding and social features aided in the model’s prediction minimally. These results highlighting the predictive potential in this area but the need for further improvement before behavioural changes can be used to reliably predict the onset of BRD in pre-weaned calves.

Quantification of play behaviour in calves using automated ultra-wideband location data and its association with age, weaning and health status

Play behaviour can act as an indicator of positive animal welfare. Previous attempts to predict play behaviour in farmed calves are limited because of the classification methods used, which lead to overestimation, and the short time periods that calves are observed. The study aimed to automatically classify and quantify play behaviour in farmed calves using location data from ultra-wide band sensors and to investigate factors associated with play behaviour. Location data were collected from 46 calves in three cohorts for a period of 18 weeks. Behavioural observations from video footage were merged with location data to obtain a total of 101.36 h of labelled data. An AdaBoost ensemble learning algorithm was implemented to classify play behaviour. To account for overestimation, generally seen in low-prevalence behaviours, an adjusted count technique was applied to the outputs of the classifier. Two generalized linear mixed models were fitted to investigate factors (e.g. age, health) associated with duration of play and number of play instances per day. Our algorithm identified play behaviour with > 94% accuracy when evaluated on the test set with no animals used for training, and 16% overestimation, which was computed based on the predicted number of samples of play versus the number of samples labelled as play on the test set. The instances and duration of play behaviour per day significantly decreased with age and sickness, whilst play behaviour significantly increased during and after weaning. The instances of play also significantly decreased as mean temperature increased. We suggest that the quantification method that we used could be used to detect and monitor other low prevalence behaviours (e.g. social grooming) from location data, including indicators of positive welfare.

Evaluation of the accuracy of a UWB tracker for in-home positioning for older adults

The population of older adults is rapidly growing. In-home monitoring systems have been used to support aging-in-place. Ambient sensors or wearable localizers can be used but may be too low resolution, while camera systems are invasive to privacy. Ultra-wideband (UWB) localization offers precise positioning by placing anchors throughout the house and wearing a tag that is tracked by the anchors. In this study, the accuracy of UWB for indoor tracking was evaluated in a motion capture gait lab and in a mock condo in the Glenrose Rehabilitation Hospital. First, the configuration of UWB was tested, changing factors related to sampling time, anchor placement and line-of-sight. Comparing these factors to the configurations recommended by the manufacturer guidelines, accuracies remained within 14 cm. We then performed static and dynamic accuracy tests, with dynamic testing comprised of rolling and walking motions. In the motion capture lab, we found localization accuracies of 7.0 ± 11.1 cm while in the mock condo, we found accuracies of 27.3 ± 12.9 cm. Dynamic testing with rolling motions had an average of 19.1 ± 1.6 cm while walking was 20.5 ± 4.2 cm. The mean accuracy of UWB is within the 30 cm target for indoor localization.

Research on indoor positioning system algorithm based on UWB technology

Ultra wideband (UWB) indoor positioning technology is attracting significant attention in the present realm of indoor wireless positioning technology due to its numerous advantages, including low energy consumption, exceptional wireless transmission rates, remarkable resolution, and powerful anti-interference capabilities.This paper applies the time of arrival (TOA) location technique, derived from the research on UWB location method, to calculate the distance between the base station and the tag using the signal flight time. By incorporating the bilateral two-way ranging algorithm, the system has been fine-tuned to lower the precision demands on the hardware.A novel positioning algorithm is introduced, utilizing both C-Taylor and adaptive robust Kalman filter, which is based on the Time of Arrival (TOA) method. This algorithm aims to address the limitations of Chan and Taylor algorithms and minimize the non-line-of-sight (NLOS) error. This algorithm has excellent theoretical research value and practical application value.

WiFi-Aided Ultra Wideband Localization in Indoor NLoS Environment

WiFi-Aided Ultra Wideband Localization in Indoor NLoS Environment

Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment.

This research delves into advancing an ultra-wideband (UWB) localization system through the integration of filtering technologies (moving average (MVG), Kalman filter (KF), extended Kalman filter (EKF)) with a low-pass filter (LPF). We investigated new approaches to enhance the precision and reduce noise of the current filtering methods-MVG, KF, and EKF. Using a TurtleBot robotic platform with a camera, our research thoroughly examines the UWB system in various trajectory situations (square, circular, and free paths with 2 m, 2.2 m, and 5 m distances). Particularly in the square path trajectory with the lowest root mean square error (RMSE) values (40.22 mm on the X axis, and 78.71 mm on the Y axis), the extended Kalman filter with low-pass filter (EKF + LPF) shows notable accuracy. This filter stands out among the others. Furthermore, we find that integrated method using LPF outperforms MVG, KF, and EKF consistently, reducing the mean absolute error (MAE) to 3.39% for square paths, 4.21% for circular paths, and 6.16% for free paths. This study highlights the effectiveness of EKF + LPF for accurate indoor localization for UWB systems.

Fusion of Time-of-Flight Based Sensors with Monocular Cameras for a Robotic Person Follower

Human-robot collaboration (HRC) is becoming increasingly important in advanced production systems, such as those used in industries and agriculture. This type of collaboration can contribute to productivity increase by reducing physical strain on humans, which can lead to reduced injuries and improved morale. One crucial aspect of HRC is the ability of the robot to follow a specific human operator safely. To address this challenge, a novel methodology is proposed that employs monocular vision and ultra-wideband (UWB) transceivers to determine the relative position of a human target with respect to the robot. UWB transceivers are capable of tracking humans with UWB transceivers but exhibit a significant angular error. To reduce this error, monocular cameras with Deep Learning object detection are used to detect humans. The reduction in angular error is achieved through sensor fusion, combining the outputs of both sensors using a histogram-based filter. This filter projects and intersects the measurements from both sources onto a 2D grid. By combining UWB and monocular vision, a remarkable 66.67% reduction in angular error compared to UWB localization alone is achieved. This approach demonstrates an average processing time of 0.0183s and an average localization error of 0.14 meters when tracking a person walking at an average speed of 0.21 m/s. This novel algorithm holds promise for enabling efficient and safe human-robot collaboration, providing a valuable contribution to the field of robotics.

Path Planning of Multiple Quadrotors with Ultrawideband Localization for Warehouse Inventory Management

The paper aims to investigate the effectiveness of ultrawideband localization in the path planning of multiple quadrotors performing inventory management in warehouses. As studies investigating warehouse automation became prevalent, researchers have looked into integrating quadrotors to aid inventory management with their ability to equip a wide array of sensors and reach high altitudes. Indoor localization has been one of the main foci of research where simultaneous localization and mapping (SLAM) and radio frequency identification (RFID) were commonly used due to their reliability and accuracy. Ultrawideband (UWB) can be viable for quadrotor navigation with their effectiveness even in nonideal lighting conditions and its relatively inexpensive infrastructure. A path planning algorithm was designed which optimizes quadrotor energy consumption and yaw movement. Simulations were performed to verify the feasibility of the system, and field experiments were carried out to assess the accuracy of UWB to localize the quadrotor. Results have shown that UWB maintained an accuracy of 0.43 m with 95% reliability, even if two quadrotors were used. Moreover, the quadrotor energy consumption was minimized successfully, with the maximum energy consumption difference below 23% for multiple inventory management.

Exploiting redundancy for UWB anomaly detection in infrastructure-free multi-robot relative localization.

Ultra-wideband (UWB) localization methods have emerged as a cost-effective and accurate solution for GNSS-denied environments. There is a significant amount of previous research in terms of resilience of UWB ranging, with non-line-of-sight and multipath detection methods. However, little attention has been paid to resilience against disturbances in relative localization systems involving multiple nodes. This paper presents an approach to detecting range anomalies in UWB ranging measurements from the perspective of multi-robot cooperative localization. We introduce an approach to exploiting redundancy for relative localization in multi-robot systems, where the position of each node is calculated using different subsets of available data. This enables us to effectively identify nodes that present ranging anomalies and eliminate their effect within the cooperative localization scheme. We analyze anomalies created by timing errors in the ranging process, e.g., owing to malfunctioning hardware. However, our method is generic and can be extended to other types of ranging anomalies. Our approach results in a more resilient cooperative localization framework with a negligible impact in terms of the computational workload.

Evidence of personality-dependent plasticity in dairy calf movement behaviours derived from automated data collection

Individual consistency in behaviour, known as animal personality, and behavioural plasticity in response to environmental changes are important factors shaping individual behaviour. Correlations between them, called personality-dependent plasticity, indicate that personality can affect individual reactions to the environment. In farm animals this could impact the response to management changes or stressors but has not yet been investigated. Here we use ultra-wideband location sensors to measure personality and plasticity in the movement of 90 dairy calves for up to 56 days starting in small pair-housing enclosures, and subsequently moved to larger social housings. For the first time calves were shown to differ in personality and plasticity of movement when changing housing. There were significant correlations between personality and plasticity for distance travelled (0.57), meaning that individuals that travelled the furthest in the pair housing increased their movement more in the social groups, and for residence time (− 0.65) as those that stayed in the same area more decreased more with the change in housing, demonstrating personality-dependent plasticity. Additionally, calves conformed to their pen-mate’s behaviour in pairs, but this did not continue in the groups. Therefore, personality, plasticity and social effects impact how farm animals respond to changes and can inform management decisions.

An ultra wideband (UWB) localization system for automatic guided vehicles (agvs) in smart warehouses

As the main component of indoor positioning technology, UWB technology plays an important role in promoting the development of indoor and outdoor seamless positioning technology. This paper presents a proposed localization system of a propagation channel measurement campaign performed in a smart warehouse environment whose dimension is 100 meters * 100 meters and provides an extensive model for this environment. Three different arrangements of basestations are simulated to analyze the effects of basestation constellations and virtual racks are set up to determine the approximate line-of-sight (LOS) probability. At the same time, by considering the influences of varying transmit power, using weighting and using two-way ranging, the distribution of errors and the reliability threshold are drawn and the required bandwidths are determined. Finally, according to the simulation results, the optimal layout and modulation parameters are recommended, and improvements in channel models and algorithms for future work are concluded.

A compact Balanced Antipodal Vivaldi Antenna with improved phase center stability for imaging and ultrawideband localization applications

This paper introduces a phase center stability improved Balanced Antipodal Vivaldi Antenna (BAVA) loaded with ‘I’ shaped non-resonant director array. The proposed antenna operates from 6 GHz to 18 GHz with S11 less than −10 dB and addresses the issue of phase reversal. The rectification of phase reversal produces enhanced phase center stability. Standard deviation is used to find least dispersed set of the phase center span calculated by varying design parameters of the director through a parametric study. By effective circumventing of phase reversal through improved coupling between the signal and ground planes, the proposed antenna achieves a maximum of 65.31% of improvement of phase center stability when compared to the reported Vivaldi antennas. In addition, further fine tuning of H-plane phase center stability for phi-components of about 6.5% at higher frequencies of operation is also attained using a meander-line section in the feed. With a size of 3.2λ0×1.6λ0×0.02032λ0 mm3, the proposed antenna exhibits sustained radiation patterns. Where λ0 is free space wavelength at 12 GHz. Full wave simulation of the design using CST microwave studio and experimental validation with a fabricated prototype are carried out.

IMU/UWB Fusion Method Using a Complementary Filter and a Kalman Filter for Hybrid Upper Limb Motion Estimation.

Motion capture systems have enormously benefited the research into human-computer interaction in the aerospace field. Given the high cost and susceptibility to lighting conditions of optical motion capture systems, as well as considering the drift in IMU sensors, this paper utilizes a fusion approach with low-cost wearable sensors for hybrid upper limb motion tracking. We propose a novel algorithm that combines the fourth-order Runge-Kutta (RK4) Madgwick complementary orientation filter and the Kalman filter for motion estimation through the data fusion of an inertial measurement unit (IMU) and an ultrawideband (UWB). The Madgwick RK4 orientation filter is used to compensate gyroscope drift through the optimal fusion of a magnetic, angular rate, and gravity (MARG) system, without requiring knowledge of noise distribution for implementation. Then, considering the error distribution provided by the UWB system, we employ a Kalman filter to estimate and fuse the UWB measurements to further reduce the drift error. Adopting the cube distribution of four anchors, the drift-free position obtained by the UWB localization Kalman filter is used to fuse the position calculated by IMU. The proposed algorithm has been tested by various movements and has demonstrated an average decrease in the RMSE of 1.2 cm from the IMU method to IMU/UWB fusion method. The experimental results represent the high feasibility and stability of our proposed algorithm for accurately tracking the movements of human upper limbs.

Digital Twin 3D System for Power Maintenance Vehicles Based on UWB and Deep Learning

To address the issue of the insufficient safety monitoring of power maintenance vehicles during power operations, this study proposes a vehicle monitoring scheme based on ultra wideband (UWB) and deep learning. The UWB localization algorithm employs Chaotic Particle Swarm Optimization (CSPO) to optimize the Time Difference of Arrival (TDOA)/Angle of Arrival (AOA) locating scheme in order to overcome the adverse effects of the non-visual distance and multipath effects in substations and significantly improve the positioning accuracy of vehicles. To solve the problem of the a large aspect ratio and the angle in the process of power maintenance vehicle operation situational awareness in the mechanical arm of the maintenance vehicle, the arm recognition network is based on the You Only Look Once version 5 (YOLOv5) and modified by Convolutional Block Attention Module (CBAM). The long-edge definition method with circular smoothing label, SIoU loss function, and HardSwish activation function enhance the precision and processing speed for the arm state. The experimental results show that the proposed CPSO-TDOA/AOA outperforms other algorithms in localization accuracy and effectively attenuates the non-visual distance and multipath effects. The recognition accuracy of the YOLOv5-CSL-CBAM network is substantially improved; the mAP value of the vehicles arm reaches 85.04%. The detection speed meets the real-time requirement, and the digital twin of the maintenance vehicle is effectively realized in the 3D substation model.