Enhanced object tracking with received signal strength using Kalman filter in sensor networks

Abstract

The importance of localization task has drawn much attention to location estimation and object tracking systems in wireless sensor networks. Many methods have been proposed to improve the location accuracy in which received signal strength (RSS) values of sensor nodes are used as an indicator of the distance between sensor node and the source node. Some of the previously proposed tracking algorithms are based on Kalman Filtering (KF) which makes us capable of tracking the location of a mobile node (MN). In this paper, the implementation and enhancement of a tracking system based on RSS indicator with the aid of an Extended Kalman Filter (EKF) is described and an adaptive filter is derived. The dynamic characteristic of channel requires considering the variations of path loss exponent of the space. Fast variations in the movement path of the source node can explicitly interrupt the performance of the localization because of inappropriate initial conditions. This imperfect behavior of the initially modelled EKF is improved and the simulation results are provided to assess the achieved enhancement. It is shown that MSE of the proposed algorithm is considerably lower than other modelled EKFs and that in presence of high measurement noise or with fewer sensor nodes this method clearly outperforms the conventional approach.