Distributed Fusion Estimator over Sensor Networks with Stochastic Event-Triggered Scheduling

This paper deals with the state estimation fusion problem for stochastic continuous-time systems over wireless sensor networks (WSNs) with multi-sensor scheduling. Due to the power limitation and communication constraints in WSNs, a closed-loop stochastic event-triggered mechanism is designed to reduce the redundant data transmission. Since this design preserves the Gaussian property of the conditional distribution of the system state, an exact minimum mean square error (MMSE) estimator could be presented for every sensor subsystem. Then, the distributed event-triggered information fusion estimator is proposed based on maximum a posterior probability criterion by a matrix-weighted combination of all available local estimates from sensor subsystems. The proposed distributed algorithm has advantages on reliability, computation efficiency due to the netted parallel structure. A numerical simulation is conducted to illustrate the effectiveness of the proposed distributed estimator.