Sensor selection based on the fisher information of the Kalman filter for target tracking in WSNs

Abstract

Target tracking in wireless sensor networks (WSNs) requires efficient collaboration among sensors to achieve the tradeoff between energy consumption and tracking accuracy requirements. In this paper, we present a sensor selection measure based on the Fisher information matrix (FIM) of the Kalman filter for target tracking in wireless sensor networks. After obtaining the target state estimate using the combination of maximum likelihood estimation and the Kalman filter, the leader of the current tracking cluster selects the most informative cluster of sensors based on the FIM-based measure to track the moving tracking at the next time. Simulation results show that the improved tracking performance of our proposed collaborative tracking approach compared to other existing methods in terms of tracking accuracy.