Distributed State Estimation Under Random Parameters and Dynamic Quantizations Over Sensor Networks: A Dynamic Event-Based Approach

Abstract

This paper deals with the distributed state estimation problem for an array of discrete time-varying systems over sensor networks under dynamic event-based transmission scheme (DETS), random parameter matrices (RPMs) and dynamic measurement quantization (DMQ). Different from the existing static event-based transmission scheme with fixed threshold, the employed DETS introduces an auxiliary offset variable in the triggering condition to dynamically regulate the inter-event time. The RPMs are considered in both state and observation equations so as to better reflect the engineering reality. A dynamic quantizer is utilized to account for the phenomenon of incomplete measurements during the data transmission. The aim of the addressed problem is to design a distributed state estimator such that, in the simultaneous presence of the DETS, RPMs and DMQ, an upper bound is guaranteed on the estimation error covariance, and such an upper bound is minimized at each time-step by choosing proper gain matrices. To overcome the difficulties induced by the sparseness of the network topology, a matrix simplification technique is proposed. Moreover, a sufficient condition is provided to ensure that the estimation error is bounded in the mean-square sense. Finally, an illustrative example is presented to demonstrate the theoretical results.