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The algorithm uses extended Kalman filter as the fusion positioning filter of WiFi\u2013pedestrian 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\u2009m. <\/jats:p>","DOI":"10.1177\/1550147719851932","type":"journal-article","created":{"date-parts":[[2019,5,25]],"date-time":"2019-05-25T06:13:32Z","timestamp":1558764812000},"page":"155014771985193","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Research on improved indoor positioning algorithm based on WiFi\u2013pedestrian dead reckoning"],"prefix":"10.1177","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9050-1284","authenticated-orcid":false,"given":"Guanghua","family":"Zhang","sequence":"first","affiliation":[{"name":"Northeast Petroleum University, Daqing, China"}]},{"given":"Xue","family":"Sun","sequence":"additional","affiliation":[{"name":"Northeast Petroleum University, Daqing, 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