Research on UWB Indoor Positioning Algorithm under the Influence of Human Occlusion and Spatial NLOS

Ultra-wideband (UWB) time-of-flight (TOF)-based ranging information in a non-line-of-sight (NLOS) environment can display significant forward errors, which directly affect positioning performance. NLOS has been a major factor limiting the improvement of UWB positioning accuracy and its application in complex scenarios. Therefore, in order to weaken the influence of the indoor complex environment on the NLOS environment of UWB and to further improve the performance of positioning, in this paper, we first analyze the factors and characteristics of NLOS formation in an indoor environment. The NLOS is divided into fixed NLOS influenced by spatial structure and dynamic random NLOS influenced by human occlusion. Then, the anchor LOS/NLOS information map is established by making full use of indoor spatial a priori information. On this basis, a robust adaptive extended Kalman filtering algorithm based on the anchor LOS/NLOS information map is designed, which is not only effectively able to exclude the influence of spatial NLOS, but can also optimize the random error. The proposed algorithm was validated in different experimental scenarios. The experimental results show that the positioning accuracy is better than 0.32 m in complex indoor NLOS environments.

[1]  P. Lou,et al.  Indoor Positioning System with UWB Based on a Digital Twin , 2022, Sensors.

[2]  Aigong Xu,et al.  Improved-UWB/LiDAR-SLAM Tightly Coupled Positioning System with NLOS Identification Using a LiDAR Point Cloud in GNSS-Denied Environments , 2022, Remote. Sens..

[3]  Xiaoji Niu,et al.  A High-Accuracy Indoor Localization System and Applications Based on Tightly Coupled UWB/INS/Floor Map Integration , 2021, IEEE Sensors Journal.

[4]  Naser El-Sheimy,et al.  Indoor navigation: state of the art and future trends , 2021, Satellite Navigation.

[5]  Xiaomin Zhu,et al.  Adapted Error Map Based Mobile Robot UWB Indoor Positioning , 2020, IEEE Transactions on Instrumentation and Measurement.

[6]  Xin Li,et al.  Research on a factor graph-based robust UWB positioning algorithm in NLOS environments , 2020, Telecommunication Systems.

[7]  Mohsen Guizani,et al.  A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact , 2020, IEEE Access.

[8]  Shau-Gang Mao,et al.  UWB System for Indoor Positioning and Tracking With Arbitrary Target Orientation, Optimal Anchor Location, and Adaptive NLOS Mitigation , 2020, IEEE Transactions on Vehicular Technology.

[9]  Xin Li,et al.  An Adaptive UWB/MEMS-IMU Complementary Kalman Filter for Indoor Location in NLOS Environment , 2019, Remote. Sens..

[10]  Kevin I-Kai Wang,et al.  Human Body Shadowing Effect on UWB-Based Ranging System for Pedestrian Tracking , 2019, IEEE Transactions on Instrumentation and Measurement.

[11]  Ole Kiel Jensen,et al.  Non-Line-of-Sight Identification for UWB Indoor Positioning Systems using Support Vector Machines , 2019, 2019 IEEE MTT-S International Wireless Symposium (IWS).

[12]  Fei Liu,et al.  An Emergency Seamless Positioning Technique Based on ad hoc UWB Networking Using Robust EKF , 2019, Sensors.

[13]  Fei Liu,et al.  An Indoor Localization Method for Pedestrians Base on Combined UWB/PDR/Floor Map , 2019, Sensors.

[14]  Kevin I-Kai Wang,et al.  A Low-Cost INS and UWB Fusion Pedestrian Tracking System , 2019, IEEE Sensors Journal.

[15]  Lei Zhang,et al.  Intelligent Positioning for a Commercial Mobile Platform in Seamless Indoor/Outdoor Scenes based on Multi-sensor Fusion , 2019, Sensors.

[16]  Xin Li,et al.  A Robust and Adaptive Complementary Kalman Filter Based on Mahalanobis Distance for Ultra Wideband/Inertial Measurement Unit Fusion Positioning , 2018, Sensors.

[17]  F. Bieth,et al.  Measurement of high-power ultra wideband signal penetration through different types of walls , 2018 .

[18]  Jian Wang,et al.  An Approach to Improve the Positioning Performance of GPS/INS/UWB Integrated System with Two-Step Filter , 2017, Remote. Sens..

[19]  Fernando Seco Granja,et al.  Comparing Ubisense, BeSpoon, and DecaWave UWB Location Systems: Indoor Performance Analysis , 2017, IEEE Transactions on Instrumentation and Measurement.

[20]  Shreyas Sundaram,et al.  Sensor selection for Kalman filtering of linear dynamical systems: Complexity, limitations and greedy algorithms , 2017, Autom..

[21]  Juan D. Tardós,et al.  ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras , 2016, IEEE Transactions on Robotics.

[22]  Tero Säntti,et al.  Map matching by using inertial sensors: literature review , 2015 .

[23]  Mattia Zorzi,et al.  Robust Kalman Filtering Under Model Perturbations , 2015, IEEE Transactions on Automatic Control.

[24]  Axel Barrau,et al.  The Invariant Extended Kalman Filter as a Stable Observer , 2014, IEEE Transactions on Automatic Control.

[25]  Tao Li,et al.  Using Indoor Maps to Enhance Real-time Unconstrained Portable Navigation , 2014 .

[26]  Aboelmagd Noureldin,et al.  Augmented Kalman Filter and Map Matching for 3D RISS/GPS Integration for Land Vehicles , 2012 .

[27]  Marcus Foth,et al.  Urban informatics , 2011, CSCW.

[28]  S. Beauregard,et al.  Indoor PDR performance enhancement using minimal map information and particle filters , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[29]  Martin Klepal,et al.  A Backtracking Particle Filter for fusing building plans with PDR displacement estimates , 2008, 2008 5th Workshop on Positioning, Navigation and Communication.

[30]  Wanli Liu,et al.  Improving Positioning Accuracy of UWB in Complicated Underground NLOS Scenario Using Calibration, VBUKF, and WCA , 2021, IEEE Transactions on Instrumentation and Measurement.

[31]  Ruizhi Chen,et al.  Smartphone-Based Indoor Positioning Technologies , 2021, Urban Informatics.

[32]  Fei Yu,et al.  Indoor Map Aiding/Map Matching Smartphone Navigation Using Auxiliary Particle Filter , 2016 .