Pedestrian Dead Reckoning and Ultrawideband Fusion Positioning Algorithm Based on Adaptive Gradient Descent
Yingbiao Yao, Yitong Song, Zhuang Wang, Zhengjing Zhou, Xin Xu, Afeng Yang · IEEE Transactions on Instrumentation and Measurement · 2025
In order to address the challenges of unknown initial positions and accumulated long-distance positioning errors in Pedestrian Dead Reckoning (PDR), as well as significant ranging errors caused by non-line-of-sight (NLOS) conditions in Ultra-Wideband (UWB) localization, this paper proposed a PDR and UWB fusion positioning algorithm based on adaptive gradient descent, briefly as AGD-PDR-UWB, for the indoor positioning scenarios where embedded terminals have limited resources. Firstly, the initial pedestrian position is estimated using the least-squares method based on UWB-ranging information from three base stations to solve the problem of unknown initial positions in PDR. Secondly, a novel method, which evaluates the degree of NLOS based on PDR positioning results and UWB-ranging information from two consecutive steps, is proposed to identify NLOS conditions in UWB signals. Finally, the proposed NLOS recognition method is used to adjust the weight of the loss function in the fusion positioning process, and the optimal pedestrian position that minimizes the loss function is obtained by the adaptive gradient descent method. Experimental results demonstrate that the proposed AGD-PDR-UWB algorithm outperforms four other comparative algorithms in two different positioning scenarios. Under the condition of three UWB base stations, the positioning error is within 0.5 m, effectively mitigating the cumulative errors of PDR and the positioning errors caused by NLOS conditions in UWB.