Abstract: Due to the indoor positioning errors produced by the unsteadiness of received WiFi signal for fingerprint-based WLAN location technique, a new positioning technology by fusing inertial measuring unit(IMU) and WiFi wireless signals with fading-factor-based extended Kalman filter (EKF) is proposed. A multiple restrictions for peak-valley detection is developed on acceleration for real-time step recognition. Then the paper utilizes the feature of indoor environment to amend the orientation for getting a correct heading angle. Finally, this paper proposes a fading-factor-based EKF fusion model based on displacement constraint with WiFi and inertial sensors positioning techniques for user’s location estimation. The experimental result shows that， this algorithm can effectively suppress the unsteadiness of jump or centralization, and enhance the indoor location robustness and reliability. The average positioning accuracy is about two meters.

Key words: indoor positioning； WiFi location, pedestrian dead reckoning, multiple sensor fusion