Title: WLAN indoor positioning method based on gradient boosting and particle filtering

Authors: Libin Hu; Zhongtao Li; Xinghai Yang; Changzhi Wei

Addresses: Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan, China ' Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan, China ' Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan, China ' Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan, China

Abstract: Indoor positioning technology has shown its great application prospects in smart cities. The main purpose of this paper is to study a low-cost, low-error indoor positioning method that can get a accurate indoor position when communicating with wireless local area networks (WLAN). The paper optimises the traditional WLAN indoor positioning method based on location fingerprint database, and algorithms about in indoor signal simulation, similarity matching of vector and continuously-positioning are tested in work of this paper, and a WLAN indoor positioning method based on gradient boosting and particle filtering is proposed. The paper finally shows the indoor positioning result with an average error of 1.7 metres. These research results verify the feasibility of WLAN indoor positioning and show that the positioning accuracy will be improved with the further optimisation of the positioning method. The potential application values of WLAN technology make it more convenient for internet of things (IoT) technology.

Keywords: IOT-SCT; gradient boosting; particle filter; WLAN indoor positioning.

DOI: 10.1504/IJSPM.2019.106170

International Journal of Simulation and Process Modelling, 2019 Vol.14 No.6, pp.535 - 545

Received: 07 Aug 2018
Accepted: 29 Apr 2019
Published online: 01 Apr 2020 *

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