Ultra-wideband Positioning System Research Articles

Smart Home for Supporting Elderly Based On Ultrawideband Positioning System

In 2017, the rate of dependency among the elderly was reported to be at 13.28%, which was problematic, due to the limited number of caregivers to assist them at all times. To address this issue, a robotic service and vital sign-based system were developed, but it was found to be insufficient for monitoring the activities of the elderly. Therefore, this study aimed to address the high dependency rates of elderly individuals who required constant support and care to survive by designing an ultrawideband-based positioning system. The system consisted of five sub-systems, including an indoor positioning system, a database system, a data processing system, an actuator system, and an application user interface. The system testing phase revealed several important findings, including that the position coordinates of the elderly were accurately read with differences of only 98.884 mm and 279.94 under Line of Sight and Non-Line of Sight conditions, respectively. Furthermore, the initial error rate of 164.39% was successfully reduced to only 1.096% by applying the average filter method in the data processing system. The actuator system also showed an impressive accuracy rate of 98% success, while the Android-based application user interface received a high user experience rate of 92.3%. Overall, these findings suggested that the ultrawideband-based positioning system had significant potential to support smart homes for the elderly and improve their quality of life.

Improved Kalman Filtering Algorithm Based on Levenberg-Marquart Algorithm in Ultra-Wideband Indoor Positioning.

To improve the current indoor positioning algorithms, which have insufficient positioning accuracy, an ultra-wideband (UWB) positioning algorithm based on the Levenberg-Marquardt algorithm with improved Kalman filtering is proposed. An alternative double-sided two-way ranging (ADS-TWR) algorithm is used to obtain the distance from the UWB tag to each base station and calculate the initial position of the tag by the least squares method. The Levenberg-Marquardt algorithm is used to correct the covariance matrix of the Kalman filter, and the improved Kalman filtering algorithm is used to filter the initial position to obtain the final position of the tag. The feasibility and effectiveness of the algorithm are verified by MATLAB simulation. Finally, the UWB positioning system is constructed, and the improved Kalman filter algorithm is experimentally verified in LOS and NLOS environments. The average X-axis and the Y-axis positioning errors in the LOS environment are 6.9 mm and 5.4 mm, respectively, with a root mean square error of 10.8 mm. The average positioning errors for the X-axis and Y-axis in the NLOS environment are 20.8 mm and 18.0 mm, respectively, while the root mean square error is 28.9 mm. The experimental results show that the improved algorithm has high accuracy and good stability. At the same time, it can effectively improve the convergence speed of the Kalman filter.

An integrated positioning method with IMU/UWB based on geometric constraints of foot-to-foot distances

Zero velocity update aided inertial navigation system (INS) and ultra-wideband (UWB) technology are widely used in pedestrian positioning. However, INS has significant cumulative error and the application environment of UWB positioning system is limited. In this study, an integrated positioning method with IMU/UWB based on geometric constraints of foot-to-foot distances is proposed. The gait of pedestrian is divided into three phases: stance, swing, and stride. Then, stance phase correction model, swing phase ranging model and stride phase size model are established, respectively. The magnetic induction intensity, accelerations, velocities, and the foot-to-foot distances measured by UWB are used to construct the constraints. Extended Kalman filter (EKF) and error state Kalman filter (ESKF) are utilized to estimate the attitudes and positions optimally. Several experiments in various environments are carried out to verify the proposed method. The results show that the proposed method has high accuracy and good robustness to the environments.

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.

Factors associated with the variation and consistency of social network position in group-housed calves

Gregarious animals often choose to interact with other individuals based on characteristics such as sex, age, and body size. However, we know little about the social structure and development of group-housed dairy calves. The aim of this study was to determine factors influencing a calves’ social network position and assess individual consistency over time. Holstein calves were group-housed (9 groups; 10 calves/group) at 15 ± 2.9 d of age (mean ± SD). Calves were weaned over 10 d beginning at 47 ± 2.5 d of age (mean ± SD) and remained in the study until 1 week after weaning. Continuous positional data were recorded within the pen using an ultra-wideband positioning system. Non-directed weighted matrices representing the social networks of calves (heifer: n = 55; bull: n= 32; excluded calves: n = 3) were constructed for each group consisting of consecutive 4-day periods per week over a total of 6 weeks. Social network position was measured using three centrality measures: strength, eigenvector, and closeness. To determine the factors that influence social structure, LMMs were used to test fixed effects of sex, ADG, age at grouping, season (warm or cool), and stage of development (pre-weaning, weaning, and post-weaning), with group and calf ID as nested random factors. Sex, weekly ADG, age at grouping, and season weren’t significant predictors of strength centrality. However, stage of development was a significant predictor for strength (p <.001) and closeness centrality (p <.001), with the highest scores found during the pre-weaning period and lowest during post-weaning. This suggests that calves were associating with more individuals in the early weeks of group formation and were more discerning with social interactions as time went on. The only predictor for eigenvector centrality was season (p =.04), with calves exhibiting higher eigenvector centrality during the cooler months. Strength and closeness centrality measures were significantly repeatable across weekly social networks for all groups (strength: R = 0.31 - R = 0.85, closeness: R = 0.27 - R = 0.89) and consistent across stage of development for 6 of the 9 groups (strength: R = 0.38 - R = 0.80, closeness: R = 0.33 - R = 0.87). Our results suggest that the amount of time spent within a group may play a role in shaping associations in dairy calves, but that dynamics of sociality and network structure may also be driven by individual and life-history traits.

Self-Sustaining Ultrawideband Positioning System for Event-Driven Indoor Localization

Self-Sustaining Ultrawideband Positioning System for Event-Driven Indoor Localization

Snake optimizer LSTM-based UWB positioning method for unmanned crane.

Position determination is a critical technical challenge to be addressed in the unmanned and intelligent advancement of crane systems. Traditional positioning techniques, such as those based on magnetic grating or encoders, are limited to measuring the positions of the main carriage and trolley. However, during crane operations, accurately determining the position of the load becomes problematic when it undergoes swinging motions. To overcome this limitation, this paper proposes a novel Ultra-Wide-Band (UWB) positioning method for unmanned crane systems, leveraging the Snake Optimizer Long Short-Term Memory (SO-LSTM) framework. The objective is to achieve real-time and precise localization of the crane hook. The proposed method establishes a multi-base station and multi-tag UWB positioning system using a Time Division Multiple Access (TDMA) combined with Two-Way Ranging (TWR) scheme. This system enables the acquisition of distance measurements between the mobile tag and UWB base stations. Furthermore, the hyperparameters of the LSTM network are optimized using the Snake Optimizer algorithm to enhance the accuracy and effectiveness of UWB positioning estimation. Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.

Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects.

Automation of transportation will play a crucial role in the future when people driving vehicles will be replaced by autonomous systems. Currently, the positioning systems are not used alone but are combined in order to create cooperative positioning systems. The ultra-wideband (UWB) system is an excellent alternative to the global positioning system (GPS) in a limited area but has some drawbacks. Despite many advantages of various object positioning systems, none is free from the problem of object displacement during measurement (data acquisition), which affects positioning accuracy. In addition, temporarily missing data from the absolute positioning system can lead to dangerous situations. Moreover, data pre-processing is unavoidable and takes some time, affecting additionally the object's displacement in relation to its previous position and its starting point of the new positioning process. So, the prediction of the position of an object is necessary to minimize the time when the position is unknown or out of date, especially when the object is moving at high speed and the position update rate is low. This article proposes using the long short-term memory (LSTM) artificial neural network to predict objects' positions based on historical data from the UWB system and inertial navigation. The proposed solution creates a reliable positioning system that predicts 10 positions of low and high-speed moving objects with an error below 10 cm. Position prediction allows detection of possible collisions-the intersection of the trajectories of moving objects.

Improved Strong Tracking Cubature Kalman Filter for UWB Positioning.

For the problems of Non-Line-of-Sight (NLOS) observation errors and inaccurate predictive dynamics model in wireless ultra-wideband (UWB) positioning systems, an improved strong tracking cubature Kalman filter (ISTCKF) positioning algorithm is proposed in this paper. The main idea of the algorithm is as follows. First, the observations are reconstructed based on the weighted positioning results obtained from the predictive dynamics model and the least squares algorithm. Second, the difference in statistical properties between the observation noise and the NLOS errors is utilized to identify the NLOS observations by the corresponding judgment statistics obtained from the operation between the original observations and the reconstructed observations. The main positioning error of the UWB positioning system at the current moment is then judged by the NLOS identification results, and the corresponding fading factors are calculated according to the judgment results. Finally, the corresponding ISTCKF is constructed based on the fading factors to mitigate the main positioning error and obtain accurate positioning result in the UWB positioning system. In this paper, the reconstructed observations mitigate the observation noise in the original observation, and then the ISTCKF mitigates the main errors in the UWB positioning system. The experimental results show that the ISTCKF algorithm reduces the positioning error by 55.2%, 32.3% and 28.9% compared with STCKF, ACKF and RSTCKF, respectively. The proposed ISTCKF algorithm significantly improves the positioning accuracy and stability of the UWB system.

Research on the Positioning Accuracy of the Cutting Head of a Tunneling Machine Based on Ultra-Wideband Positioning Technology

Directed at the problems of low positioning accuracy and irregular section forming of cutting heads of road header in coal mine production sites, a new cutting head positioning system based on ultra-wideband positioning technology is proposed based on the cutting head motion model and the working principle of ultra-wideband positioning technology, which verifies the anti-interference and the accuracy of its positioning. Combined with the simulation experiment under on-site working conditions, the influence degree of three typical influencing factors on positioning accuracy was obtained, and the accuracy optimization of the ultra-wideband positioning system was guided. Through the dynamic solution experiment, the positioning accuracy of the system is measured, and the results are verified based on the positioning system solution accuracy evaluation standard.

Research on IMU-Assisted UWB-Based Positioning Algorithm in Underground Coal Mines.

The application of an ultra-wideband (UWB) positioning system in a Global Positioning System (GPS) denial environment such as an underground coal mine, mainly focuses on position information and rarely involves information such as direction attitude. Position accuracy is often affected by multipath, non-visible ranges, base station layout, and more. We proposed an IMU-assisted UWB-based positioning system for the provision of positioning and orientation services to coal miners in underground mines. The Error-State Kalman Filter (ESKF) is used to filter the errors in the measured data from the IMU-assisted UWB positioning system to obtain the best estimate of the error for the current situation and correct for inaccuracies due to approximations. The base station layout of the IMU-assisted UWB positioning system was also simulated. The reasonable setting of the reference base station location can suppress multi-access interference and improve positioning accuracy to a certain extent. Numerous simulation experiments have been conducted in GPS denial environments, such as underground coal mines. The experimental results show the effectiveness of the method for determining the position, direction, and attitude of the coal miner under the mine, which provides a better reference value for positioning and orientation in a GPS rejection environment such as under the mine.

A Lightweight CIR-Based CNN With MLP for NLOS/LOS Identification in a UWB Positioning System

Implementing line-of-sight (LOS) and none-line-of-sight (NLOS) identification in ultra-wideband (UWB) systems is crucial. Convolutional neural network (CNN) based identification methods can extract higher-level features automatically, but they are based on channel impulse response (CIR)-turned image ingested features that impose calculation complexity and do not make use of manual features due to the data inundation risk. In this letter, we propose a novel multilayer perceptron (MLP)-based LOS/NLOS identification algorithm that can utilize both manually extracted features and feature from CNN based on raw CIR inputs with only 7.39% calculation complexity as compared to the traditional image-based CNN. Three experiments, at a teaching building, an office, and an underground mine, were conducted to verify the proposed method’s performance. Our proposed features are conducive to LOS/NLOS identification, especially the proposed raw CIR-based feature from the CNN, achieving 26.9% improvement over existing manual features. Furthermore, the proposed method outperformed the traditional image-based CNN with an improvement of 44.16%.

Remote monitoring method for human body electrostatic potential based on symbolic regression machine learning

Remote real-time monitoring of the human body electrostatic potential is of great value to the investigation, analysis, and prevention of electrostatic hazard accidents. The non-contact measurement method inverses the body electrostatic potential by detecting the surrounding electrostatic field. The distribution of electrostatic fields around the human body is easily influenced by the placement of metal equipment and the architectural structure in the application scenario. Therefore, physical modeling-based inversion lacks generality. Field-measured electrostatic signals and symbolic regression machine learning are used to remotely monitor body electrostatic potential. In a 25 m2 laboratory, four non-contact electrostatic sensors, a contact-type body voltage measuring system, and an ultra-wideband positioning system were used to establish the experiment setting. Sixty sets of on-site test data from three participants were used for model training and performance evaluation. The results indicate that the normalized root-mean-square errors of the body electrostatic potential ranged from 0.01 to 0.22. The optimal results satisfy the IEC 61340-4-5:2018 criteria for the precision of the body potential measuring system.

Estimation of Measurement Uncertainty of the Real-Time Location System (RTLS) with Ultra-Wideband (UWB) Technology

The need to control the real-time location of assets is increasingly relevant worldwide. The Ultra-wideband (UWB) technology is an IoT solution for real-time locating systems (RTLS). The location of the asset is obtained by the signal exchange between a wireless tag (asset) and fixed anchors. The tag interacts with the fixed anchors, defining its position through the distances obtained by trilateration. This data is sent to the server through the gateway. It is well-known that this process has several sources of errors. However, the measurement uncertainty assessment of UWB technology is an important topic regarding its scope of use. This paper presents a task-specific measurement uncertainty evaluation for the UWB positioning system, according to the ISO GUM. It aims to propose a method to support decision-making regarding the possible uses of UWB technology. The position provided by the UWB is compared with reference points using Cartesian coordinates that are measured with a total station, providing metrological reliability. Using the information from the estimated uncertainty, one can define the minimum tolerance interval associated with UWB technology for a given use. A case study demonstrates the method.

Range-Extension Algorithms and Strategies for TDOA Ultra-Wideband Positioning System.

The Internet of Things (IoT) for smart industry requires the surveillance and management of people and objects. The ultra-wideband positioning system is an attractive solution for achieving centimeter-level accuracy in target location. While many studies have focused on improving the accuracy of the anchor coverage range, it is important to note that in practical applications, positioning areas are often limited and obstructed by furniture, shelves, pillars, or walls, which can restrict the placement of anchors. Furthermore, some positioning regions are located beyond anchor coverage, and a single group with few anchors may not be able to cover all rooms and aisles on a floor due to non-line-of-sight errors causing severe positioning errors. In this work, we propose a dynamic-reference anchor time difference of arrival (TDOA) compensation algorithm to enhance accuracy beyond anchor coverage by eliminating local minima of the TDOA loss function near anchors. We designed a multidimensional and multigroup TDOA positioning system with the aim of broadening the coverage of indoor positioning and accommodating complex indoor environments. By employing an address-filter technique and group-switching process, tags can seamlessly move between groups with a high positioning rate, low latency, and high accuracy. We deployed the system in a medical center to locate and manage researchers with infectious medical waste, demonstrating its usefulness for practical healthcare institutions. Our proposed positioning system can thus facilitate precise and wide-range indoor and outdoor wireless localization.

A Dynamic Window-Based UWB-Odometer Fusion Approach for Indoor Positioning

The positioning system based on ultrawideband (UWB) can achieve centimeter-level positioning accuracy, which is now widely used for indoor positioning. However, UWB is subject to multipath effects and nonline-of-sight (NLOS), which can cause positioning errors. And a UWB positioning system with a minimum of three anchors is costly and difficult to deploy, which is not an economical option. This article presents a low-cost UWB-odometer fusion method for mobile robot localization, which enables global localization using only one UWB anchor. To improve the positioning accuracy and eliminate the influence of cumulative odometer error, we propose a dynamic window-based particle filter (DWBPF). It solves the problem of particle convergence and track loss. It also adaptively modifies the score weights according to the power difference of the UWB to achieve a more robust and accurate positioning. The experimental result shows that the system has a high positioning accuracy of 0.061 m.

UWB Positioning System Based on LSTM Classification With Mitigated NLOS Effects

It is known that an ultrawideband (UWB)-based indoor positioning system (IPS) has superior positioning performance and can meet the requirements of location-based services (LBSs) as the Internet of Things (IoT) applications. However, there is a limitation of UWB positioning when it is conducted at the nonline-of-sight (NLOS) channels degrading the UWB ranging accuracy at indoor environments. In this article, we propose an artificial intelligence (AI) applied UWB positioning system that can enhance the positioning performance by classifying channel conditions with channel impulse response (CIR) of the received UWB signal. The proposed system mitigates the positioning degradation caused by the NLOS situations by performing extended Kalman filter (EKF) localization and long short-term memory (LSTM) training of the observed channel status. The main feature of the proposed UWB positioning method is that it can be used even at unknown locations not trained with the LSTM model learning the channel status, because of our training strategy of not the position coordinates, but the UWB ranging error between UWB devices corresponding to CIR of the received UWB signal. This article provides the experimental setup and performance evaluation results of the proposed system. The evaluation results showed that the proposed AI-applied UWB positioning method significantly improved its accuracy performance compared with the existing positioning methods.

UWB Anchor Node Optimization Deployment Using MGWO for the Coal Mine Working Face End

Appropriate deployment of anchor nodes (ANs) is an essential prerequisite for achieving high-precision positioning. To ameliorate the comprehensive positioning performance, the weight position dilution of precision (WPDOP), that is utilized to evaluate the appropriateness of the ANs placement geometry is proposed by taking into account the different error variances for each AN. A simple closed-form calculation WPDOP method is theoretically derived for UWB positioning system by utilizing elementary symmetric polynomials and Newton’s identity, which is applied to evaluate the Ultra-wideband (UWB) deployment at the end of coal mine working face (CMWF). Several basic ANs layout strategies, based on the projection shape in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x-y</i> plane, are designed for the end of CMWF, and the modified grey wolf optimizer (MGWO) algorithm is executed to optimize the ANs deployment scheme in order to attain the better configuration and higher positioning accuracy. Simulation and experiments verification are simultaneously performed via the UWB positioning system. The experimental results sufficiently demonstrate that MGWO deployment scheme offers lower average WPDOP values than the obtained ones when applying random placement, and the lowest average improvement percentage was approximately equal to 37.6%. Furthermore, after implementing the MGWO algorithm, the V-shape deployment configuration is capable to provide the lowest average WPDOP values and outperforms the other arrangements, keeping the lowest localization error. This is regarded as the best deployment and it gives significant guidance for the ANs sensor deployment at the end of CMWF.

Research on AGV positioning method combined with IMU and UWB

Aiming at the problem that automated guided vehicle (AGV) is difficult to locate accurately due to the influence of environment and time drift when it works in the indoor intelligent storage system. In this paper, an extended kalman filtering (EKF) framework is designed. In order to make full use of the original ranging values of ultra wideband (UWB) and inertial measurement unit (IMU), the framework realizes the fusion positioning between UWB module and IMU module in a tight coupling manner, so as to ensure that the system can still work when the available base station signal is inaccurate. Firstly, for the problem that the traditional UWB positioning method is easily affected by the non-line of sight (NLOS) error indoors, the calculated positioning coordinate value is unstable. With the help of different NLOS probability distribution curves of different obstacles, the weighted least square method is applied to the UWB positioning method to determine the positioning coordinate value of UWB, which improves the sudden change of AGV positioning coordinate in the static environment. Then the data fusion algorithm is optimized, and the error value of IMU and UWB coordinate is taken as the observation value of EKF, which reduces the influence of cumulative error on IMU positioning results, provides the global optimal estimation of the system optimal state, and improves the fusion positioning accuracy. Finally, the measured data of UWB and IMU systems in indoor complex environment are simulated in MATLAB. The experimental results show that when NLOS signal seriously affects the positioning effect, the UWB and IMU combined positioning system can provide more reliable positioning results than the single IMU positioning system. It improves the positioning accuracy of AGV and provides a new idea for indoor positioning mode.

Research on multi-sensor fusion-based AGV positioning and navigation technology in storage environment

Aiming at the problems of low positioning accuracy and large trajectory deviation when AGVs work in dynamic storage environments, an AGV positioning and navigation method based on multi-sensor data fusion is studied. The method is based on traceless Kalman filter (UKF) and adaptive Monte Carlo localisation (AMCL) algorithms to fuse three kinds of sensor data, namely LiDAR, inertial guidance system and ultra-wideband positioning system, to finally output the accurate attitude of the AGV [1]. Remoting data communication between the AGV and the host computer through TCP protocol, and the host computer remotely controls the AGV to achieve autonomous positioning and navigation after the sensor data is fused. Experiments are carried out on a MATLAB simulation experiment platform, and the results show that the positioning and navigation system has high positioning accuracy and trajectory fitting, and has certain practicality in dynamic storage environment.