Distributed state estimation for uncertain sensor networks with mixed time delays subject to sensor saturations

Abstract

In this paper, the distributed state estimation problem is investigated for a class of uncertain sensor networks. The target plant is described by a set of uncertain difference equations with both discrete-time and infinite distributed delays, where two random variables are introduced to account for the randomly occurring nonlinearities. The sensor measurement outputs are subject to randomly occurring sensor saturations due to the physical limitations of the sensors. Through available output measurements from each individual sensor and its neighboring sensors, this paper aims to design distributed state estimators to approximate the states of the target plant in a distributed way. Sufficient conditions are presented which not only guarantee the estimation error systems to be globally asymptotically stable in the mean square sense but also ensure the existence of the desired estimator gains.

Event-triggered distributed state estimation with randomly occurring uncertainties and nonlinearities over sensor networks: A delay-fractioning approach

“…where ς k,l = ξ T k ζ T l T , Π 1 , Π 2 , Π 3 , Λ 1 , Λ 2 and Λ 3 are defined in (21). According to (19)- (20), we can conclude that the estimation error system (16) is mean-square asymptotically stable.…”

“…whereα andβ are defined in (21). Similarly, through straightforward algebraic manipulations, we have…”

“…where Ω, C and Ψ are defined in (21). Then, together with (18), (20), (23)-(32), for two scalars λ 1 > 0 and λ 2 > 0, we have…”

“…So far, a number of approaches have been developed to deal with the analysis and synthesis problems for time-delay systems. It should be pointed out that, so far, much effort has been made to design the state estimators for time-delay systems over sensor networks [18,20,21]. To mention a few, in [18], the Gaussian mixture Kalman particle filtering algorithm has been developed to cope with the Gaussian or non-Gaussian nature of the random network delays.…”

“…To mention a few, in [18], the Gaussian mixture Kalman particle filtering algorithm has been developed to cope with the Gaussian or non-Gaussian nature of the random network delays. The distributed state estimation problems have been investigated in [20,21] for discrete nonlinear systems with time-delays and nonlinear disturbances.…”