WIFI/PDR indoor integrated positioning system in a multi-floor environment

Abstract

To improve the accuracy of indoor positioning for location-based services, we created an improved WiFi/PDR integrated positioning and navigation system where we are using Extended Kalman filter (EKF). The proposed algorithm first relies on MEMS in our mobile phone to estimate the velocity and heading angles of the target. Second, the velocity and heading angles, together with the results of WiFi fingerprinting-based positioning, are considered as the input of the EKF for the sake of conducting two-dimensional positioning. Third, the proposed algorithm calculates the altitude of the target by using the real-time recorded barometer. The results of our experiments show that integrated navigation system using Extended Kalman filter can effectively eliminate the accumulated errors in the PDR positioning algorithm and can reduce the influence of the large-scale jump of the WiFi fingerprint positioning result brought by the RSSI disturbance on the positioning accuracy of the system.