Research on High Precision Positioning Method for Pedestrians in Indoor Complex Environments Based on UWB/IMU

Abstract

Location information is the core data in IoT applications, which is the essential foundation for scene interpretation and interconnection of everything, and thus high-precision positioning is becoming an immediate need. However, the non-line-of-sight (NLOS) effect of indoor complex environment on UWB signal occlusion has been a major factor limiting the improvement in ultra-wideband (UWB) positioning accuracy, and the optimization of NLOS error has not yet been studied in a targeted manner. To this end, this paper deeply analyzes indoor scenes, divides NLOS into two forms of spatial occlusion and human occlusion, and proposes a particle filtering algorithm based on LOS/NLOS mapping and NLOS error optimization. This algorithm is targeted to optimize the influence of two different forms of NLOS, using spatial a priori information to accurately judge the LOS/NLOS situation of the anchor, optimizing the NLOS anchor ranging using IMU to project the virtual position, judging whether the LOS anchor is affected by human occlusion, and correcting the affected LOS anchor using the established human occlusion error model. Through experimental verification, the algorithm can effectively suppress two different NLOS errors of spatial structure and human occlusion and can achieve continuous and reliable high-precision positioning and tracking in complex indoor environments.

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

“…Solutions for dynamic (pedestrian) localization incorporate NLoS detection/mitigation strategies into tracking algorithms (i.e., filters), which estimate a new position for each new range measurement while taking previous measurements into account. State-of-the-art solutions have added IMUs to their pedestrian tracking algorithms [25,26,28]. By integrating the inertial data [25] or by employing a PDR algorithm [26,28], these systems combine inertial localization systems unaffected by NLoS conditions but prone to drift errors with absolute UWB positioning.…”

“…State-of-the-art solutions have added IMUs to their pedestrian tracking algorithms [25,26,28]. By integrating the inertial data [25] or by employing a PDR algorithm [26,28], these systems combine inertial localization systems unaffected by NLoS conditions but prone to drift errors with absolute UWB positioning. On top of that, active NLoS detection was added based on the estimated walking direction and distance derived from IMU data in [25,26], but the orientation itself was not used for HBS mitigation.…”

“…On top of that, active NLoS detection was added based on the estimated walking direction and distance derived from IMU data in [25,26], but the orientation itself was not used for HBS mitigation. The authors of [28] do differentiate between spatial obstructions and HBS by respectively mapping the obstructions and by using an IMU to estimate the relative user orientation. By incorporating different mitigation strategies for each NLoS type into an adaptive Extended Kalman Filter (EKF), [28] improved the latter approach by 40%.…”

“…However, these methods employ the IMU to predict positions using Pedestrian Dead Reckoning (PDR) [26] or the integration of the inertial data [25], independent of any type of UWB NLoS condition. The few works that do perform orientation-aware UWB HBS mitigation have clear limitations [27,28], which are discussed in Section 2. Therefore, our work addresses this general lack of research on this topic and improves on the limitations of the few existing related works.…”

Attitude solving aided UWB/IMU integrated algorithm with real-time NLOS suppression

“…Wang et al [14] propose a fitting NLOS recognition method based on distance prediction of TOA, which can detect NLOS nodes in real time and adaptively adjust the judgment threshold of NLOS nodes, the simulation experiments have demonstrated the algorithm's resistance to interference and stability. In the face of complex indoor environments where multiple NLOS nodes coexist and more complex localization trajectories, better real-time and robustness requirements need to be met to achieve recognition suppression of NLSO errors [2].…”