UWB/INS Integrated Pedestrian Positioning for Robust Indoor Environments

• Published in: IEEE sensors journal Vol. 20; no. 23; pp. 14401 - 14409
• Main Authors: Zhang, Yuan, Tan, Xinglong, Zhao, Changsheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Data integration; Distance measurement; Filtering algorithms; Indoor environments; Indoor navigation; Inertial navigation; INS; Navigation systems; NLOS; Pedestrian indoor positioning; Robot localization; Robustness; State estimation; Ultra wideband technology; Ultrawideband; UWB; UWB/INS integrated navigation
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.2998815

Goal: Accurate robust ultra wideband (UWB) pedestrian indoor positioning becomes a challenging task when faced with robust indoor environments such as non-line of sight (NLOS) due to refraction of signals, multipath effect, etc. When the UWB system is faced with such a robust environment, its positioning accuracy is difficult to reach the centimeter level and the reliability of the system operation needs to be improved. In order to solve such problems, an ultra wideband/inertial navigation system (UWB/INS) integrated pedestrian navigation algorithm is proposed. On the one hand, UWB-based joint state estimation particle filtering is used for position calculation, and on the other hand, INS-based zero-speed update (ZUPT) algorithm is used for navigation information solution. Under the framework of the INS error equation, the navigation information fusion of the two systems is carried out. In the simple pedestrian environment, the average positioning accuracy of the UWB/INS algorithm is 53.8% higher than that of the UWB algorithm, 40% higher than the INS algorithm and 31% higher than Original UWB/INS algorithm. Under the robust pedestrian indoor positioning, the average positioning accuracy of the UWB/INS algorithm is 39.7% higher than that of the UWB algorithm, 37.5% higher than the INS algorithm and 53% higher than Original UWB/INS algorithm. The results of two sets of pedestrian indoor positioning experiments demonstrate the effectiveness of our approach.