Suboptimal Filtering Over Sensor Networks With Random Communication

• 成果类型: Article
• 署名作者: Tanwani, Aneel
• 署名单位: Universite de Toulouse; Centre National de la Recherche Scientifique (CNRS)
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3116180
• 发表日期: 2022
• 页码: 5456-5463
• 关键词: Covariance matrices; Noise measurement; Differential equations; Stochastic systems; Filtering; dynamical systems; CONVERGENCE; Distributed estimation and filtering; graph theory; heterogenous agents; random communication; stochastic hybrid system

摘要

The problem of filter design is considered for continuous-time linear stochastic systems using distributed sensors. Each sensor unit, represented by a node in an undirected and connected graph, collects some information about the state and communicates its own estimate with the neighbors. It is stipulated that this communication between sensor nodes connected by an edge is time-sampled randomly and for each edge, the sampling process is an independent Poisson counter. Our proposed filtering algorithm for each sensor node is a stochastic hybrid system: It comprises a continuous-time differential equation, and at random time instants when communication takes place, each sensor node updates its state estimate based on the information received by its neighbors. In this setting, we compute the expectation of the error covariance matrix for each unit which is governed by a matrix differential equation. To study the asymptotic behavior of these covariance matrices, we show that if the gain matrices are appropriately chosen and the mean sampling rate is large enough, then the error covariances practically converge to a constant matrix.