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.
Highlights
With the development of the times, people are increasingly demanding positioning technology
The widely used Global Positioning System (GPS) has changed the way of outdoor vehicle and pedestrian navigation, while its performance may deteriorate dramatically in the underground or indoor environment due to the existence of serious signal blocking and complicated multipath fading,[2] China began to develop the Beidou satellite navigation system in 1994 independently
The Beidou satellite navigation system is superior than the GPS, especially in the message function
Summary
With the development of the times, people are increasingly demanding positioning technology. The state transition is used to identify the walking cycle for the step counting, but the cumulative error of the PDR algorithm cannot be reduced.[23] In the literature,[25] an indoor positioning algorithm based on inertial measurement is proposed.
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