Sensor Scheduling in Distributed Kalman Filter for Multi-Target Tracking

This paper studies the design of a distributed sensor scheduling policy for a sensor network, in which each dynamical target can only be measured by partial sensors due to the restriction of sensor resources while each sensor requires to monitor all targets. Consensus Kalman filtering algorithm and stochastic scheduling strategy are applied. Firstly, a necessary condition of the observation probabilities of the targets, which can guarantee the boundedness of the expected covariance of the network, is provided. Secondly, the marginal utility of the expected covariance with respect to the observation probability is proved. Then, an algorithm is proposed to compute the optimal probabilities, which requires less complex calculations. Numerical simulations are conducted to demonstrate the performance of the proposed algorithms.