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

Abstract

Location-based services (LBS) are services offered through a mobile device that take into account a device’s geographical location. To provide position information for these services, location is a key process. The creation of systems for solving problems of positioning and navigation inside buildings is a very perspective, actual and complicated task, especially in a multi-floor environment. To improve the accuracy of indoor positioning for location-based services, we created an improved WiFi/PDR (Pedestrian Dead Reckoning) 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 (2D) positioning. Third, the proposed algorithm calculates the altitude of the target by using the real-time recorded barometer and geographic data. Tests were conducted on two floors of the building to achieve three-dimensional (3D) positioning in multi-floor environment using proposed integrated WiFi/PDR positioning algorithm. 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. In a real multi-floor environment, the proposed algorithm of WiFi/PDR integrated system has a mean error of positioning accuracy is 1.6m, which is much less than the 10m of the WiFi alone positioning result, and the 2m of the PDR alone positioning result.