Event-Based Variance-Constrained ${\mathcal {H}}_{\infty }$ Filtering for Stochastic Parameter Systems Over Sensor Networks With Successive Missing Measurements

Abstract

This paper is concerned with the distributed filtering problem for a class of discrete time-varying stochastic parameter systems with error variance constraints over a sensor network where the sensor outputs are subject to successive missing measurements. The phenomenon of the successive missing measurements for each sensor is modeled via a sequence of mutually independent random variables obeying the Bernoulli binary distribution law. To reduce the frequency of unnecessary data transmission and alleviate the communication burden, an event-triggered mechanism is introduced for the sensor node such that only some vitally important data is transmitted to its neighboring sensors when specific events occur. The objective of the problem addressed is to design a time-varying filter such that both the requirements and the variance constraints are guaranteed over a given finite-horizon against the random parameter matrices, successive missing measurements, and stochastic noises. By recurring to stochastic analysis techniques, sufficient conditions are established to ensure the existence of the time-varying filters whose gain matrices are then explicitly characterized in term of the solutions to a series of recursive matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of the developed event-triggered distributed filter design strategy.

Distributed filtering for nonlinear time‐delay systems over sensor networks subject to multiplicative link noises and switching topology

“…An illustrative simulation example has been provided to verify the validity of the proposed distributed filtering strategy. One of the future research topics would be to extend the main results to more complicated systems with more performance requirements …”

“…One of the future research topics would be to extend the main results to more complicated systems with more performance requirements. 9,[43][44][45][46]…”

H∞ finite‐horizon filtering for complex networks with state saturations: The weighted try‐once‐discard protocol

“…Complex networks (CNs) are made up of a set of individual nodes connected in terms of certain topological rules, which can describe various kinds of real‐world networks such as social systems, scientific citation web, cyber‐physical systems, ecosystems, and neural networks . In order to better understand the CNs and pursue some desired performance specifications, it is necessary to obtain the state information of all network nodes.…”

Robust finite-horizon filtering for nonlinear time-delay Markovian jump systems with weighted try-once-discard protocol

“…Shi, Boukas, and Agarwal (1999), Boukas (2009), Chen, Niu, andZou (2013), Chen, Niu, and Zou (2014), Xu, Lam, and Mao (2007) and Ma, Wang, Han, and Liu (2017). Among varies of filter design algorithms, the H ∞ filtering approach has been exploited to handle systems subject to external noise signals with bounded energy but unknown statistics (Ma, Wang, Han, & Lam, 2018;Wang, Wang, Han, & Wei, 2018;Zhang, Wang, Ding, & Liu, 2015). For instance, in Chen et al (2013), the adaptive sliding mode control problem has been investigated for a class of Markovian jump systems with actuator degradation.…”