Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization  

作　　者：Qiu Wanqing Zhang Qingmiao Zhao Junhui Yang Lihua 

机构地区：[1]School of Information Engineering,East China Jiaotong University,Nanchang 330013,China [2]School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China

出　　处：《China Communications》2024年第5期113-122,共10页中国通信（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(U2001213 and 61971191);in part by the Beijing Natural Science Foundation under Grant L182018 and L201011;in part by National Key Research and Development Project(2020YFB1807204);in part by the Key project of Natural Science Foundation of Jiangxi Province(20202ACBL202006);in part by the Innovation Fund Designated for Graduate Students of Jiangxi Province(YC2020-S321)。

摘　　要：Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

关 键 词：extreme learning machine fingerprinting localization indoor localization machine learning particle swarm optimization 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TN92[自动化与计算机技术—控制科学与工程]