Relaying Kalman filters for range-based sensor networks

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. Compared with the simple fusion(SF) method, the RKF algorithm has better performance, but at the cost of its computational complexity. Finally, simulation results show the effectiveness of the proposed algorithm.