Adaptive Event-Triggered Quantized Communication-Based Distributed Estimation Over Sensor Networks With Semi-Markovian Switching Topologies

This paper presents a distributed state estimation method for nonlinear systems over sensor networks with SemiMarkovian switching topologies (S-MSTs). An adaptive eventtriggered quantization scheme (AETQS) is developed to reduce the communication and computation burden for bandwidthconstrained sensor networks, where the quantified measurement data is determined by the specific event triggering condition. The filtering network topology evolves over time, which is assumed to be governed by a Semi-Markov chain. Based on the Semi-Markov kernel theory and Lyapunov stability theory, sufficient conditions are obtained to guarantee that the error dynamics has sigma-error mean square stability and H-infinity performance, which is given in the form of linear matrix inequalities. Then, the optimal disturbance attenuation level, initial triggering thresholds, and elapsed-timedependent distributed filter gains can be determined by addressing a convex optimization problem. Finally, two numerical examples are presented to verify the effectiveness of the proposed approach.

Automated summary

This paper presents a distributed state estimation method for nonlinear systems over sensor networks with Semi-Markovian switching topologies (S-MSTs). An adaptive event-triggered quantization scheme (AETQS) is developed to reduce the communication and computation burden for bandwidth-constrained sensor networks. The optimal disturbance attenuation level, initial triggering thresholds, and elapsed-time-dependent distributed filter gains can be determined by addressing a convex optimization problem. Two numerical examples are presented to verify the effectiveness of the proposed approach.