An efficient sensor scheduling for target tracking

In this paper we study an efficiently and simple algorithm for mobile sensor management in moving target tracking problem. The novel method leads to reduce comparison number of sensor sequences and executive time for unconstrained optimization. It results in reduce memory utilization and computational cost as well. The Kalman Filter technique is applied to predict posteriori state of target. In addition, the propagation environment is additive white Gaussian noise (AWGN) channel. Numerical example reveals this algorithm can obtain the optimal scheduling by an uncomplicated implementation.