Integrated PDR/fingerprinting indoor location tracking with outdated radio map

Abstract

This paper presents an indoor location tracking algorithm that integrates pedestrian dead reckoning (PDR) positioning and fingerprinting positioning. The Kalman filter is applied for the integration of two different positioning approaches. In practice, received signal strength (RSS) significantly varies by not only environmental changes, but also device types and device orientations. Due to the RSS variation problem, the radio map constructed in the offline phase of the fingerprinting positioning becomes outdated or inaccurate. The outdated radio map leads to unreliable fingerprinting positioning results, and the errors are contained in the tracking results. A RSS transformation method is proposed which scales the online RSS according to the difference from the offline RSS to obtain more reliable fingerprinting positioning results with the outdated radio map. The proposed algorithm is implemented into an Android-based smartphone and evaluated in a real environment. Through the experimental results, it is shown that the proposed algorithm enables higher accuracy than the Kalman filter-based location tracking algorithm without the RSS transformation.