Ultra-wideband Anchors Research Articles

BDS-3 RTK/UWB semi-tightly coupled integrated positioning system in harsh environments

Real-time kinematic (RTK) positioning is a commonly used technique in modern industry, which is limited by problems such as signal occlusion, attenuation, and multipath, especially in complex urban canyons. To maintain the consistency of centimeter-level accuracy, we adopt the ultra-wideband (UWB) enhanced BDS-3 RTK positioning algorithm. This paper proposed a semi-tightly coupled (STC) BDS-3 RTK/UWB integration positioning model. This model realizes the UWB and BDS-3 complement each other and integrate information in the position domain. Besides, height constraint is imposed on UWB positioning to mitigate the effect of poor positioning of UWB in height components. To verify the effectiveness of the above algorithm, we have compared and analyzed the positioning performance of the STC BDS-3 RTK/UWB integration model and single BDS-3 RTK model in different occlusion environments. The positioning performance of static and kinematic of BDS-3 RTK/UWB STC based on different number of UWB anchors is further analyzed. The real-world experiment results show that the positioning accuracy of the proposed method can reach centimeter-level. Moreover, the proposed model can obtain more accurate positioning results than those of using single system, and it shows more obvious advantages, especially in the occlusion environment. In the occlusion environment, the root means square error in the east, north, and up directions is improved from (0.629 m, 0.325 m, 1.160 m) of the BDS-3-only to (0.075 m, 0.074 m, 0.029 m). This study can provide a reference for the future development of high-precision, real-time, continuous positioning, navigation, and timing in complex urban environments.

Robust IMU-Based Mitigation of Human Body Shadowing in UWB Indoor Positioning.

Ultra-wideband (UWB) indoor positioning systems have the potential to achieve sub-decimeter-level accuracy. However, the ranging performance degrades significantly under non-line-of-sight (NLoS) conditions. The detection and mitigation of NLoS conditions is a complex problem and has been the subject of many works over the past decades. When localizing pedestrians, human body shadowing (HBS) is a particular and specific cause of NLoS. In this paper, we present an HBS mitigation strategy based on the orientation of the body and tag relative to the UWB anchors. Our HBS mitigation strategy involves a robust range error model that interacts with a tracking algorithm. The model consists of a bank of Gaussian Mixture Models (GMMs), from which an appropriate GMM is selected based on the relative body-tag-anchor orientation. The relative orientation is estimated by means of an inertial measurement unit (IMU) attached to the tag and a candidate position provided by the tracking algorithm. The selected GMM is used as a likelihood function for the tracking algorithm to improve localization accuracy. Our proposed approach was realized for two tracking algorithms. We validated the implemented algorithms on dynamic UWB ranging measurements, which were performed in an industrial lab environment. The proposed algorithms outperform other state-of-the-art algorithms, achieving a 37% reduction of the p75 error.

Ultra Wideband (UWB) Localization Using Active CIR-Based Fingerprinting

Indoor positioning systems using Ultra Wideband (UWB) achieve high positioning accuracy (<30 cm). However, traditional localization approaches require many packet exchanges (e.g. two-way ranging) or challenging clock synchronization (e.g. time difference of arrival). To remedy this, we propose active fingerprinting using the channel impulse response (CIR) from a single UWB packet received at each UWB anchor. The proposed neural network anchor-subset selection method with Savitzky-Golay filter achieves a low mean absolute error (20.9-87.0 cm), in contrast to signal strength based fingerprinting approaches that realize accuracies of 2-3 m. Finally, with CIR interpolation the data collection overhead is reduced.

Adaptive Decentralized Cooperative Localization for Firefighters Based on UWB and Autonomous Navigation

Cooperative localization (CL) is a popular research topic in the area of localization. Research is becoming more focused on Unmanned Aerial Vehicles (UAVs) and robots and less on pedestrians. This is because UAVs and robots can work in formation, but pedestrians cannot. In this study, we develop an adaptive decentralized cooperative localization (DCL) algorithm for a group of firefighters. Every member maintains a local filter and estimates the position and the relative measurement noise covariance is estimated rather than a fixed value. We derived the explicit expressions for the inter-member collaboration instead of using approximations. This method reduces the influence of non-line-of-sight (NLOS) errors in the ultra-wideband (UWB) ranging on the CL, eliminating the need for fixed UWB anchors. The proposed algorithm was validated by two experiments designed in the building and forest environments. The experimental results demonstrate that the proposed algorithm improved the accuracy of localization, and the proposed algorithm suppressed the localization errors by 14.23% and 47.01% compared to the decentralized cooperative localization extended Kalman filter (DCLEKF) algorithm, respectively.

Fusion of GNSS Pseudoranges with UWB Ranges Based on Clustering and Weighted Least Squares.

Global navigation satellite systems (GNSSs) and ultra-wideband (UWB) ranging are two central research topics in the field of positioning and navigation. In this study, a GNSS/UWB fusion method is investigated in GNSS-challenged environments or for the transition between outdoor and indoor environments. UWB augments the GNSS positioning solution in these environments. GNSS stop-and-go measurements were carried out simultaneously to UWB range observations within the network of grid points used for testing. The influence of UWB range measurements on the GNSS solution is examined with three weighted least squares (WLS) approaches. The first WLS variant relies solely on the UWB range measurements. The second approach includes a measurement model that utilizes GNSS only. The third model fuses both approaches into a single multi-sensor model. As part of the raw data evaluation, static GNSS observations processed with precise ephemerides were used to define the ground truth. In order to extract the grid test points from the collected raw data in the measured network, clustering methods were applied. A self-developed clustering approach extending density-based spatial clustering of applications with noise (DBSCAN) was employed for this purpose. The results of the GNSS/UWB fusion approach show an improvement in positioning performance compared to the UWB-only approach, in the range of a few centimeters to the decimeter level when grid points were placed within the area enclosed by the UWB anchor points. However, grid points outside this area indicated a decrease in accuracy in the range of about 90 cm. The precision generally remained within 5 cm for points located within the anchor points.

Cooperative Localization of Firefighters Based on Relative Ranging Constraints of UWB and Autonomous Navigation

There are many demands for the cooperative localization (CL) of multiple people, such as firefighter rescue. The classical foot-mounted inertial navigation based on zero velocity update (ZUPT) suffers from accumulating error due to the low-cost inertial sensor, and the pre-placed anchors in the ultra-wideband (UWB) system limit the application in an unknown environment. In this study, a group of sensors including the inertial measurement unit (IMU), magnetometer, barometer, and UWB sensor is used. Through the different characteristics of sensors and the position relationship between people, a cooperative localization system using an extended Kalman filter for three-dimensional firefighter tracking is proposed. Ranging information between firefighters from UWB is utilized, and couplings introduced by relative measurement are estimated. Two experiments are designed to verify the proposed algorithm in building and forest environments. Compared with the results of single-person inertial navigation, the average positioning precision of the algorithm in the building and forest is, respectively, improved by 38.93% and 79.01%. This approach successfully suppresses the divergence of positioning errors, and fixed UWB anchors are not needed.

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 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.

NLOS Identification- and Correction-Focused Fusion of UWB and LiDAR-SLAM Based on Factor Graph Optimization for High-Precision Positioning with Reduced Drift

In this study, we propose a tightly coupled integrated method of ultrawideband (UWB) and light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) for global navigation satellite system (GNSS)-denied environments to achieve high-precision positioning with reduced drift. Specifically, we focus on non-line-of-sight (NLOS) identification and correction. In previous work, we utilized laser point cloud maps to identify and exclude NLOS measurements in real time to attenuate their severe effects on the integrated system. However, the complete exclusion of NLOS measurements will likely lead to deterioration in the dilution of precision (DOP) for the remaining line-of-sight (LOS) anchors, counterproductively introducing large positioning errors into the integrated system. Therefore, this study considers the ranging accuracy and geometric distribution of UWB anchors and innovatively proposes an NLOS correction method using a grey prediction model. For a poor line-of-sight (LOS) anchor geometric distribution, the grey prediction model is used to fill in the gaps by predicting the NLOS measurements based on historical measurements. Including the corrected measurements effectively improves the original poor geometric configuration, improving the system positioning accuracy. Since conventional filtering-based fusion methods are exceedingly sensitive to measurement outliers, we use state-of-the-art factor graph optimization (FGO) to tightly integrate the UWB measurements (LOS and corrected measurements) with LiDAR-SLAM. The temporal correlation between measurements and the redundant system measurements effectively enhance the robustness of the integrated system. Experimental results show that the tightly coupled integrated method combining NLOS correction and FGO improves the positioning accuracy under a poor geometric distribution, increases the system availability, and achieves better positioning than filtering-based fusion methods with a root-mean-square error of 0.086 m in the plane direction, achieving subdecimeter indoor high-precision positioning.

Indoor scenario-based UWB anchor placement optimization method for indoor localization

Currently, ultra-wideband (UWB)-based localization systems are attracting much attention due to their high ranging accuracy. It is well known that the relative sensor–source geometry and non-line-of-sight (NLOS) measurement can severely impact the accuracy of the location estimate. To improve the localization accuracy, we propose an anchor placement optimization method that is based on the corresponding indoor scenario. Specifically, concrete walls affect the ranging error and constrain the placement of UWB sensors. An indoor scenario modeling method is presented for generating NLOS ranging paths and dividing the search space. Furthermore, a stochastic/deterministic combination ranging model is proposed based on the through-the-wall path and Gaussian process regression technique. We propose a heuristic differential evolution algorithm for searching for an optimal anchor placement that is based on minimizing the Cramer–Rao lower bound (CRLB) and predicting the ranging error. In addition, a region division method and a region combination method are proposed for reducing the search space, optimizing anchor placement in parallel in different regions, and providing full line-of-sight (LOS) coverage regions to the greatest extent possible. Finally, field experiments demonstrate the effectiveness of the proposed indoor scenario modeling method and ranging model. The proposed indoor scenario-based UWB anchor placement method achieves decimeter-level localization accuracy. A thorough comparison confirms the localization performance improvement attributed to the proposed anchor placement method.

Establishing the correspondences between 2D UWB positions and image coordinates

Ultra wide band (UWB) positioning system and camera are two types of sensors which records positions and images respectively. In many applications, it is expected to fuse the data obtained by the two types of sensors in two different coordinate systems. In this work, we study the problem of establishing the correspondences between 2D UWB positions and image coordinates of a camera. We propose a simple yet effective method which requires simple apparatus and does not need to know in advance the positions of the UWB anchors in camera frame. Experimental results have demonstrated the effectiveness of proposed method.

Range-aided ego-centric collaborative pose estimation for multiple robots

Robots’ simultaneous relative pose estimation has become an essential step in most robotic-oriented problems, such as map merging, collision avoidance, path planning, and multi-Simultaneous Localization and Mapping (SLAM). This article addresses the problem of 3D and ego-centric relative pose estimation for a team of robots equipped with Ultra WideBand (UWB) nodes. More specifically, the article introduces a novel optimization framework to obtain pose information based on the embodiment of UWB ranges, without relying on any fixed external infrastructure configuration of UWB anchors on the surrounding environment. In the proposed method, we demonstrate the validity through the utilization of a Micro Aerial Vehicle (MAV) and a ground vehicle that are equipped with multiple UWB transceivers, and each platform simultaneously acts as a based anchor for the other platform for extracting an ego-centric position estimation of the UWB nodes. Additionally, for the pose estimation, the obtained information is fused with the onboard Inertial Measurement Unit (IMU) measurements on each of the considered robotic platforms. Finally, the efficacy of the proposed theoretical framework is evaluated in multiple experiments, where the aerial and ground platforms are simultaneously and separately navigating, and the ego-centric collaborative pose-estimation is compared with a VICON ground truth positioning system.

Improved-UWB/LiDAR-SLAM Tightly Coupled Positioning System with NLOS Identification Using a LiDAR Point Cloud in GNSS-Denied Environments

Reliable absolute positioning is indispensable in long-term positioning systems. Although simultaneous localization and mapping based on light detection and ranging (LiDAR-SLAM) is effective in global navigation satellite system (GNSS)-denied environments, it can provide only local positioning results, with error divergence over distance. Ultrawideband (UWB) technology is an effective alternative; however, non-line-of-sight (NLOS) propagation in complex indoor environments severely affects the precision of UWB positioning, and LiDAR-SLAM typically provides more robust results under such conditions. For robust and high-precision positioning, we propose an improved-UWB/LiDAR-SLAM tightly coupled (TC) integrated algorithm. This method is the first to combine a LiDAR point cloud map generated via LiDAR-SLAM with position information from UWB anchors to distinguish between line-of-sight (LOS) and NLOS measurements through obstacle detection and NLOS identification (NI) in real time. Additionally, to alleviate positioning error accumulation in long-term SLAM, an improved-UWB/LiDAR-SLAM TC positioning model is constructed using UWB LOS measurements and LiDAR-SLAM positioning information. Parameter solving using a robust extended Kalman filter (REKF) to suppress the effect of UWB gross errors improves the robustness and positioning performance of the integrated system. Experimental results show that the proposed NI method using the LiDAR point cloud can efficiently and accurately identify UWB NLOS errors to improve the performance of UWB ranging and positioning in real scenarios. The TC integrated method combining NI and REKF achieves better positioning effectiveness and robustness than other comparative methods and satisfactory control of sensor errors with a root-mean-square error of 0.094 m, realizing subdecimeter indoor positioning.

Position Estimation and Error Correction of Mobile Robots Based on UWB and Multisensors

Since there are many interferences in the indoor environment, it is difficult to achieve the precise positioning of the mobile robot using a single sensor. This paper presents a position estimation and positioning error correction method of mobile robots based on multisensor data. The robot’s positioning sensor includes ultra-wideband (UWB) components, inertial measurement unit (IMU), and encoders. UWB multipath interference causes more ranging errors, which can be reduced by the correction equation after data fitting. The real-time coordinates of the UWB robot tag can be calculated based on multiple UWB anchor data and the least squares method. The coordinate data x c , y c are acquired by UWB positioning subsystem, and the velocity data x ̇ c , y ̇ c are collected by IMU together with encoders. The multisensor data continuously update Kalman filter and estimate robot position. In the positioning process, the positioning data of different sensors can be mutually corrected and supplemented. The results of UWB ranging correction experiments indicate that data fitting can improve the UWB positioning accuracy. In the multisensor positioning experiments, compared with a single sensor, the positioning method based on data fusion of UWB, IMU, and encoders has higher accuracy and adaptability. When UWB signals are interfered or invalid, other sensors can still work normally and complete the robot positioning process. The multisensor positioning method not only improves the robot positioning accuracy but also has stronger environmental adaptability.

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Low drift visual inertial odometry with UWB aided for indoor localization

Visual inertial odometry (VIO) would have an estimation drift problem in the process of long trajectory for indoor localization, especially in the absence of loop detection or in unknown complex scenes. To solve this problem, a low drift visual inertial odometry with ultra-wideband (UWB) aided for indoor localization was proposed. Firstly, a single UWB anchor was dropped in an unknown position, and a cost function was formed by the position information output by VIO and the UWB ranging information to obtain the position of the anchor. Then, the single anchor position and the UWB ranging constraints were added to the tightly coupled visual inertial fusion algorithm framework, thereby improving the robustness of motion tracking and reducing the drift of the odometry. Finally, the effectiveness of the proposed method was verified in the actual indoor environment, and the experiment results demonstrated that, compared with state-of-the-art localization methods, the positioning accuracy and robustness were improved significantly.

A Novel Movable UWB Localization System Using UAVs

Recently, interest in ultrawideband (UWB)-based localization systems has increased in various application fields. However, since UWB anchors are usually fixed, they work only within the limited range that UWB measurement signals can reach. To address this issue, a swarm flight system with movable UWB anchors is developed. One ground station controls multiple unmanned aerial vehicles (UAVs), each of which is equipped with a UWB anchor and real-time kinematic global positioning system (RTK-GPS) capabilities, and collects the precise (centimeter-level) positions of the UWB anchor UAVs through RTK-GPS. In this way, the constraints of existing UWB systems can be alleviated by changing the UWB anchor positions in real time as desired by the user. In addition, this paper proposes a novel localization algorithm using only time-of-flight (TOF) measurements from UWB signals, heading information from an attitude and heading reference system (AHRS), and altitude information from a barometer. The results of UAV flight tests show that the proposed algorithm provides better localization performance than existing algorithms.

A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction.

A fingerprint-based localization system is an economic way to solve an indoor positioning problem. However, the traditional off-line fingerprint collection stage is a time-consuming and laborious process which limits the use of fingerprint-based localization systems. In this paper, based on ubiquitous Wireless Fidelity (Wi-Fi) equipment and a low-cost Ultra-Wideband (UWB) ranging system (with only one UWB anchor), a ready-to-use indoor localization system is proposed to realize long-term and high-accuracy indoor positioning. More specifically, in this system, it is divided into two stages: (1) an initial stage, and (2) a positioning stage. In the initial stage, an Inertial Measure Unit (IMU) is used to calculate the position using Pedestrian Dead Reckon (PDR) algorithm within a preset number of steps, and the location-related fingerprints are collected to train a Convolutional Neural Network (CNN) regression model; simultaneously, in order to make the UWB ranging system adapt to the Non-Line-of-Sight (NLoS) environment, the increments of acceleration and angular velocity in IMU and the increments of single UWB ranging measures are correlated to pre-train a Supported Vector Regression (SVR). After reaching the threshold of time or step number, the system is changed into a positioning stage, and the CNN predicts the position calibrated by corrected UWB ranging. At last, a series of practical experiments are conducted in the real environment; the experiment results show that, due to the corrected UWB ranging measures calibrating the CNN parameters in every positioning period, this system has stable localization results in a comparative long-term range. Additionally, it has the advantages of stability, low cost, anti-noise, etc.

ULoc

A myriad of IoT applications, ranging from tracking assets in hospitals, logistics, and construction industries to indoor tracking in large indoor spaces, demand centimeter-accurate localization that is robust to blockages from hands, furniture, or other occlusions in the environment. With this need, in the recent past, Ultra Wide Band (UWB) based localization and tracking has become popular. Its popularity is driven by its proposed high bandwidth and protocol specifically designed for localization of specialized "tags". This high bandwidth of UWB provides a fine resolution of the time-of-travel of the signal that can be translated to the location of the tag with centimeter-grade accuracy in a controlled environment. Unfortunately, we find that high latency and high-power consumption of these time-of-travel methods are the major culprits which prevent such a system from deploying multiple tags in the environment. Thus, we developed ULoc, a scalable, low-power, and cm-accurate UWB localization and tracking system. In ULoc, we custom build a multi-antenna UWB anchor that enables azimuth and polar angle of arrival (henceforth shortened to '3D-AoA') measurements, with just the reception of a single packet from the tag. By combining multiple UWB anchors, ULoc can localize the tag in 3D space. The single-packet location estimation reduces the latency of the entire system by at least 3×, as compared with state of art multi-packet UWB localization protocols, making UWB based localization scalable. ULoc's design also reduces the power consumption per location estimate at the tag by 9×, as compared to state-of-art time-of-travel algorithms. We further develop a novel 3D-AoA based 3D localization that shows a stationary localization accuracy of 3.6 cm which is 1.8× better than the state-of-the-art two-way ranging (TWR) systems. We further developed a temporal tracking system that achieves a tracking accuracy of 10 cm in mobile conditions which is 4.3× better than the state-of-the-art TWR systems.

Ultra-Wideband Positioning Sensor with Application to an Autonomous Ultraviolet-C Disinfection Vehicle.

Due to the COVID-19 virus being highly transmittable, frequently cleaning and disinfecting facilities is common guidance in public places. However, the more often the environment is cleaned, the higher the risk of cleaning staff getting infected. Therefore, strong demand for sanitizing areas in automatic modes is undoubtedly expected. In this paper, an autonomous disinfection vehicle with an Ultraviolet-C (UVC) lamp is designed and implemented using an ultra-wideband (UWB) positioning sensor. The UVC dose for 90% inactivation of the reproductive ability of COVID-19 is 41.7 J/m2, which a 40 W UVC lamp can achieve within a 1.6 m distance for an exposure time of 30 s. With this UVC lamp, the disinfection vehicle can effectively sterilize in various scenarios. In addition, the high-accuracy UWB positioning system, with the time difference of arrival (TDOA) algorithm, is also studied for autonomous vehicle navigation in indoor environments. The number of UWB tags that use a synchronization protocol between UWB anchors can be unlimited. Moreover, this proposed Gradient Descent (GD), which uses Taylor method, is a high-efficient algorithm for finding the optimal position for real-time computation due to its low error and short calculating time. The generalized traversal path planning procedure, with the edge searching method, is presented to improve the efficiency of autonomous navigation. The average error of the practical navigation demonstrated in the meeting room is 0.10 m. The scalability of the designed system to different application scenarios is also discussed and experimentally demonstrated. Hence, the usefulness of the proposed UWB sensor applied to UVC disinfection vehicles to prevent COVID-19 infection is verified by employing it to sterilize indoor environments without human operation.