Event-triggered Kalman consensus filter for sensor networks with intermittent observations

摘要

This article investigates the event-triggered Kalman consensus filtering (ET-KCF) problem for distributed sensor networks with intermittent observations. First, a novel ET consensus filtering structure is designed for sensor networks with intermittent observations. With the proposed consensus filtering structure, a new ET mechanism that is more efficient than the existed ones is designed to schedule transmissions of local estimates. Then, an optimal ET-KCF in the sense of minimum mean-square error is developed. For reducing the computational complexity of filtering algorithm, a suboptimal ET-KCF is further proposed. Moreover, the stability of the suboptimal ET-KCF is analyzed. Simulation results verify the validity and superiority of the proposed method.