@inproceedings{106261f6b37e44e2a27dee185142426d,
title = "Indoor Location Algorithm Based on Migration PSO-ELM in the Absence of Training Sets",
abstract = "Ultra-wideband technology has its unique advantages and is widely used in indoor localization. UWB positioning methods based on machine learning can significantly improve localization accuracy. However, the algorithms are generally trained based on experimental data and indoor features, which is difficult to applicate in other indoor places in the absence of scene training sets. In this work, a migration localization scheme for particle swarm algorithm to optimize the extreme learning machine (Migration PSO-ELM) is presented. This positioning algorithm generates virtual training datasets to achieve accurate localization in new indoor places without experimental data. According to the simulation results, Migration PSO-ELM can maintain high accuracy and shorten the deployment time when applied in new scenes without training sets.",
keywords = "Indoor localization, Particle swarm algorithm optimization limit learning machine, Ultra-wideband",
author = "Yifan Wu and Wenhao Wang and Jing Zhang",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2022 ; Conference date: 05-08-2022 Through 07-08-2022",
year = "2023",
doi = "10.1007/978-981-19-6613-2\_376",
language = "英语",
isbn = "9789811966125",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "3864--3873",
editor = "Liang Yan and Haibin Duan and Yimin Deng and Liang Yan",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control",
address = "德国",
}