@inproceedings{a6b9940e7ff0443896ef178e3ba41751,
title = "An optimized fusion positioning algorithm based on BP neural network",
abstract = "Multi-source fusion positioning is an effective approach when the positioning accuracy is unsatisfactory. To achieve accurate and continuous pedestrian positioning in complex indoor environments, we propose an optimized fusion positioning algorithm based on BP neural network. Firstly, the average positioning error of Wi-Fi is utilized to constrain the geomagnetic matching range. Secondly, Particle Swarm Optimization (PSO) is used to optimize the BP-AdaBoost ensemble learning algorithm, then the optimized BP-AdaBoost-PSO is used to fuse Wi-Fi positioning results and constrained geomagnetic positioning results. Simulation results indicate that the constraint method can reduce the positioning error caused by the geomagnetic mismatch, and the proposed fusion positioning algorithm can reduce the average execution time and improve the positioning accuracy.",
keywords = "ensemble learning, indoor positioning, multi-source fusion, neural network, particle swarm optimization",
author = "Ao Liu and Chundi Xiu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Emergency Science and Information Technology, ICESIT 2021 ; Conference date: 22-11-2021 Through 24-11-2021",
year = "2021",
doi = "10.1109/ICESIT53460.2021.9696678",
language = "英语",
series = "Proceedings of 2021 IEEE International Conference on Emergency Science and Information Technology, ICESIT 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "244--248",
editor = "Guorong Chen",
booktitle = "Proceedings of 2021 IEEE International Conference on Emergency Science and Information Technology, ICESIT 2021",
address = "美国",
}