Event-Triggered Distributed State Estimation: A Conditional Expectation Method

This article mainly focuses on distributed filtering for a discrete time-varying system observed by a sensor network, where each sensor can measure some partial state information of the system and communicate with its neighbors. A novel distributed event-triggered communication mechanism is designed to reduce the communication rate among the sensors and guarantee the performance of the filter. With a data scheduler, the sensor is able to decide whether to transmit data to its neighbors. By applying Gaussian approximation, an evaluation of the effect caused by the nontransmission event is derived, which characterizes the tradeoff between communication rate and state estimation performance. Subsequently, a corresponding suboptimal filtering gain design protocol is proposed. Compared with the literature, the filtering algorithm proposed in this article is less conservative. Finally, numerical simulation is provided to illustrate the improvement of performance and the robustness of the approximation.