Energy-efficient fusion estimation for wireless sensor networks with packet losses

This paper is concerned with the optimal fusion estimation for a discrete-time stochastic dynamic process in wireless sensor networks (WSNs) with random packet losses. The various sensors in the WSN transmit measurements with a period that is integer multiple of the measurement sampling period to save energy, and the overall estimation system is described as a double-rate stochastic system with multiple stochastic parameters. The optimal fusion Kalman filters are designed by using the innovation analysis approach and are presented in terms of a set of recursive Riccati and Lyapunov equations. It is shown that the filtering performance critically depends on the measurement transmission period and the packet loss probabilities. An illustrative example is finally to show the effectiveness of the proposed results.