Relaying Kalman filters for sensor networks

Abstract

Due to limited sensing range for sensors, moving object tracking has to be realized by relaying from one sensor to the other in sensor networks, and so the tracking procedure can be modelled as a Markov chain system. Based on the Bayesian theory, we propose the relaying Kalman filter(RKF) algorithm which introduce the equations of updating sensor probability, and reconstruct the innovation equation. Furthermore, the extension of the proposed method can be applied in nonlinear dynamic system. Finally, simulation results show the effectiveness of the proposed RKF algorithm.