A comparative study of target tracking with Kalman filter, extended Kalman filter and particle filter using received signal strength measurements

Abstract

Tracking of wireless nodes such as robots in wireless sensor network (WSN) has been widely studied in literature. Most of these studies are based on the Kalman filter (KF) for linear models corrupted by Gaussian noise, the extended Kalman filter (EKF) for non linear models and the particles filter (PF) which does no require the model o be linear nor the noise be Gaussian. In his paper, we present a comparative study of mobile target node (TN) racking via the KF, EKF and PF based on the received power of the signal. A constant velocity model is considered for the motion of TN, depicting an indoor environment. The performance of the filters are compared in terms of the root mean square error (RMSE). Extensive simulations are performed to evaluate the performance of the discussed filters.