Distributed state estimation for a stochastic linear hybrid system over a sensor network. Issue 10 (1st July 2018)

• Record Type: Journal Article
• Authors: Deshmukh, Raj; Thapliyal, Omanshu; Kwon, Cheolhyeon; Hwang, Inseok
• Is Part Of: IET control theory & applications. Volume 12:Issue 10(2018)
• Page Start: 1456
• Page End: 1464
• Publication Date: 2018-07-01
• Subjects: linear systems -- continuous systems -- state estimation -- Markov processes -- stochastic systems -- distributed sensors -- difference equations -- discrete systems; constant transition matrix -- Markovian process -- discrete state transitions -- SLHS -- estimation information -- dynamical system -- distributed state estimation problem -- stochastic linear hybrid system -- edge‐error covariances -- local mode‐conditioned state estimates -- optimal consensus estimation -- distributed hybrid state estimation algorithm -- connected sensor agents -- sensor network topology -- distributed sensor network applications -- stochastic linear difference equations -- continuous state dynamics; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2017.1208
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : In this study, the authors consider the distributed state estimation problem of a stochastic linear hybrid system (SLHS) observed over a sensor network. The SLHS is a dynamical system with interacting continuous state dynamics described by stochastic linear difference equations and discrete state (or mode) transitions governed by a Markovian process with a constant transition matrix. Most existing hybrid estimation algorithms are based on a centralised architecture which is not suitable for distributed sensor network applications. Further, the existing distributed hybrid estimation algorithms are restrictive in sensor network topology, or approximate the consensus process among connected sensor agents. This study proposes a distributed hybrid state estimation algorithm based on the multiple model based approach augmented with the optimal consensus estimation algorithm which can locally process the state estimation and share the estimation information with the neighbourhood of each sensor agent. This shared information comprises local mode‐conditioned state estimates and edge‐error covariances, and is used to bring about an agreement or a consensus across the network. The proposed distributed hybrid state estimation algorithm is demonstrated with an illustrative aircraft tracking example.