Research Article
UWB/IMU Fusion Localization Strategy Based on Continuity of Movement
@INPROCEEDINGS{10.1007/978-3-031-34776-4_4, author={Li Zhang and Jinhui Bao and Jingao Xu and Danyang Li}, title={UWB/IMU Fusion Localization Strategy Based on Continuity of Movement}, proceedings={Mobile and Ubiquitous Systems: Computing, Networking and Services. 19th EAI International Conference, MobiQuitous 2022, Pittsburgh, PA, USA, November 14-17, 2022, Proceedings}, proceedings_a={MOBIQUITOUS}, year={2023}, month={6}, keywords={Kalman Filter (KF) inertial measurement unit (IMU) ultrawideband (UWB) indoor positioning system (IPS)}, doi={10.1007/978-3-031-34776-4_4} }- Li Zhang
Jinhui Bao
Jingao Xu
Danyang Li
Year: 2023
UWB/IMU Fusion Localization Strategy Based on Continuity of Movement
MOBIQUITOUS
Springer
DOI: 10.1007/978-3-031-34776-4_4
Abstract
Commercial and industrial sectors are increasingly deploying inertial measurement unit (IMU) and ultrawideband (UWB) for motion control, automation, and positioning applications, such as intelligent manufacturing, smart homes and smartphones. However, it does not perform well in a multi-obstacle environment, such as the problem of locating workers in a multi-worker environment and finding cars in a large parking lot. This is because IMU can provide a low-cost and accurate inertial navigation solution in a short time, but its positioning error increases rapidly over time as a result of accumulated accelerometer measurement errors. On the other hand, even under line-of-sight (LOS) settings, UWB positioning and navigation accuracy is impacted by the real environment, resulting in unreliable leaps. Therefore, it is difficult to achieve high accuracy positioning using single positioning and navigation system in indoor environments. In this paper, a robust UWB and IMU fusion indoor localization system based on adaptive dynamic Kalman Filter (ADKF) algorithm has been proposed which relies on motion continuity and can be applied to indoor complex multipath environment. Specifically, in order to mitigate non-line-of-sight (NLOS) errors, one novel range-constrained weighted least square (RWLS) algorithm is presented. The experimental results show that both algorithms can mitigate NLOS errors effectively and reach a particular degree of robustness and ongoing tracking capability in integrated indoor positioning system (IPS).
