Application of Extended Kalman Filter for Tracking of Mobile Target

Knowledge of continuous location updates is very important aspect in many location based services (LBS). Therefore, localization and tracking a mobile target using RSSI measurements with wireless sensor network (WSN), is one of the widely research topic. The trilateration based localization using received signal strength indicators (RSSIs) is simple and widely used approach in the literature. However, high localization accuracy may not be obtained with the trilateration due to dynamicity (highly fluctuating nature) of RSSI measurements. Therefore, the location estimates of trilateration must be refined further with the help of some more advanced state estimation technique to guarantee high localization accuracy. In this paper a novel fusion of trilateration and extended kalman filter (EKF) to address the issue of uncertainties in measurement noise in the received signal strength indicators (RSSIs) is proposed named as Trilateration+EKF. The localization performance of the proposed Trilateration+EKF algorithm is compared with traditional trilateration technique in this paper. The simulation results demonstrate the efficacy of the proposed Trilateration+EKF algorithm with respect to trailateration technique in the context of dynamicity in RSSIs.