Research on improved indoor positioning algorithm based on WiFi–pedestrian dead reckoning

Guanghua Zhang, Xue Sun, Jingqiu Ren and Weidang Lu

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 5, 1550147719851932

Abstract: In order to improve the positioning accuracy and reduce the impact of indoor complex environment on WiFi positioning results, an improved fusion positioning algorithm based on WiFi–pedestrian dead reckoning is proposed. The algorithm uses extended Kalman filter as the fusion positioning filter of WiFi–pedestrian dead reckoning. Aiming at the problem of WiFi signal strength fluctuation, Bayesian estimation matching algorithm based on K -nearest neighbor is proposed to reduce the impact of the dramatic change of received signal strength indicator value on the positioning result effectively. For the cumulative error problem in pedestrian dead reckoning positioning algorithm, a post-correction module is used to reduce the error. The experimental results show that the algorithm can improve the shortcomings of these two algorithms and control the positioning accuracy within 1.68 m.

Keywords: Indoor positioning; fusion location; K-nearest neighbor; pedestrian dead reckoning; extended Kalman filter (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:5:p:1550147719851932

DOI: 10.1177/1550147719851932

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