Spherical simplex unscented Kalman filter for RSSI-Based WLAN IEEE 802.11n positioning and tracking

Abstract

Location services gained attraction with the recent advancements in context and location-aware technologies. Furthermore, location information becomes important with the deployment of wireless communication networks and the mobility that characterizes the wireless communication users. Within indoor environments, coverage of the explicit sensors based on Global Positioning System (GPS) is limited. Building an indoor location tracking system based on the Received Signal Strength Indicator (RSSI) of the widely deployed Wireless Local Area Network (WLAN) is considered cost effective. Extended Kalman Filter (EKF) is the most implemented algorithm for obtaining location information out of the RSSI measurements. In this paper, we propose the Spherical Simplex Unscented Kalman Filter (SSUKF) to work over WLAN IEEE 802.11n networks for indoor positioning and tracking. SSUKF exploits the RSSI measurements and the knowledge of anchor nodes' positions for location estimation. SSUKF is proposed for easy of implementation and reduced computational cost compared with EKF. Comparative results are illustrated using Monte Carlo simulations in MATLAB.