Exploit Kalman filter to improve fingerprint-based indoor localization

Indoor localization enjoys a wide range of applications from helping firefighters navigate through a burning building to business men trying to find a room in a complex building. The mainstream indoor localization technology is based on the wireless signal strength such as WLAN and Zigbee. However, the special characteristics of the indoor environment usually results in the large localization error. This paper proposes to improve the performance of fingerprint method in tracking a mobile user in an indoor environment. In this paper, we introduce the Kalman filter and apply it to fingerprint based indoor tracking. The experiment and simulation results show that the method improves effectively the positioning accuracy.