Multi-source fusion positioning method based on hierarchical optimization

被引：0
|
作者：
Liu A. [1 ]
Xiu C. [1 ]
机构：
[1] School of Electronic and Information Engineering, Beihang University, Beijing
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2023年 / 49卷 / 05期
关键词：
ensemble learning; indoor positioning; multi-source fusion; neural network; particle filter; particle swarm optimization;
D O I：
10.13700/j.bh.1001-5965.2021.0390
中图分类号：
学科分类号：
摘要：
To achieve accurate and continuous pedestrian positioning in complex indoor environments, we propose a multi-source fusion positioning algorithm based on hierarchical optimization is proposed. First, the geomagnetic matching range is constrained with the Wi-Fi positioning result. Afterwards, particle swarm optimization (PSO) is adopted to optimize the BP-AdaBoost ensemble learning algorithm. The optimized BP-AdaBoost-PSO is employed to fuse the Wi-Fi and the constrained geomagnetic positioning results. Particle filter (PF) is then applied to fuse the above fusion result and the pedestrian dead reckoning (PDR) result. Simulation results indicate that the proposed algorithm has sufficient robustness and can effectively improve the continuous positioning accuracy in a pedestrian motion state. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.
引用
收藏
页码：1176 / 1183
页数：7
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