Scaled Unscented Kalman Filter for RSSI-based Indoor Positioning and Tracking

Abstract

Global positioning technologies such as the Global Positioning System (GPS) are ubiquitously available for different positioning applications. Within indoor environments, coverage of the explicit sensors based on GPS is limited. Developing an indoor location tracking system based on the Received Signal Strength Indicator (RSSI) of the Wireless Local Area Network (WLAN) is considered cost effective method. The widely used technique for estimating the position out of the RSSI measurements is the Extended Kalman Filter (EKF). However, EKF has high computational complexity due to the calculation of Jacobian matrices and suffers from filer instability. In this paper, we propose the Scaled Unscented Kalman Filter (SUKF), which is one of the Sigma Point Kalman Filters (SPKF) family, to overcome the limitations of the EKF. SUKF shall work over the WLAN IEEE 802.11n networks to exploit the RSSI range measurements for localizing and tracking of a mobile node. For performance evaluation, SUKF is compared with the EKF. Results are illustrated using Monte Carlo simulation in MATLAB.