title: An Indoor Continuous Positioning Algorithm on the Move by Fusing Sensors and Wi-Fi on Smartphones.
abstract: Wi-Fi indoor positioning algorithms experience large positioning error and low stability when continuously positioning terminals that are on the move. This paper proposes a novel indoor continuous positioning algorithm that is on the move, fusing sensors and Wi-Fi on smartphones. The main innovative points include an improved Wi-Fi positioning algorithm and a novel positioning fusion algorithm named the Trust Chain Positioning Fusion (TCPF) algorithm. The improved Wi-Fi positioning algorithm was designed based on the properties of Wi-Fi signals on the move, which are found in a novel "quasi-dynamic" Wi-Fi signal experiment. The TCPF algorithm is proposed to realize the "process-level" fusion of Wi-Fi and Pedestrians Dead Reckoning (PDR) positioning, including three parts: trusted point determination, trust state and positioning fusion algorithm. An experiment is carried out for verification in a typical indoor environment, and the average positioning error on the move is 1.36 m, a decrease of 28.8% compared to an existing algorithm. The results show that the proposed algorithm can effectively reduce the influence caused by the unstable Wi-Fi signals, and improve the accuracy and stability of indoor continuous positioning on the move.
authors.affiliation: Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China. huaiyuli@pku.edu.cn.
authors.forename: Huaiyu
authors.initials: H
authors.lastname: Li
authors.affiliation: Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China. xwchen@pku.edu.cn.
authors.forename: Xiuwan
authors.initials: X
authors.lastname: Chen
authors.affiliation: National Remote Sensing Center of China, No. 8A, Liulinguan Nanli, Haidian District, Beijing 100036, China. jinggf@most.cn.
authors.forename: Guifei
authors.initials: G
authors.lastname: Jing
authors.affiliation: Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China. pkuwangy@pku.edu.cn.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China. caoyanfeng@cate.casc.|Beijing Aerospace Unmanned Vehicles System Engineering Research Institute, No. 1 Fengyingdong Road, Haidian District, Beijing 100094, China. caoyanfeng@cate.casc.
authors.forename: Yanfeng
authors.initials: Y
authors.lastname: Cao
authors.affiliation: Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China. gisor.lee@pku.edu.cn.
authors.forename: Fei
authors.initials: F
authors.lastname: Li
authors.affiliation: Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China. 1501110619@pku.edu.cn.
authors.forename: Xinlong
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China. hxiao@pku.edu.cn.
authors.forename: Han
authors.initials: H
authors.lastname: Xiao
keywords: PDR; TCPF; Wi-Fi; indoor positioning; multi-sensor fusion