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Consensus-based distributed information filter for a class of jump Markov systems

Consensus-based distributed information filter for a class of jump Markov systems

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  • Author(s): W. Li 1  and  Y. Jia 1, 2
    • View affiliations
    • Affiliations: 1: Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA), Beijing, People's Republic of China
      2: Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Ministry of Education, SMSS, Beihang University (BUAA), Beijing, People's Republic of China
  • Source: Volume 5, Issue 10, 7 July 2011, p. 1214 – 1222
    DOI:  10.1049/iet-cta.2010.0240 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Published

This study investigates the problem of distributed fusion for a class of jump Markov systems in a not fully-connected sensor network. A distributed information filter is proposed from the point of view of the consensus theory. To this end, the best-fitting Gaussian (BFG) approximation approach is applied to overcome the difficulty of lacking a global model for multiple model estimation fusion, and a recursive formula is presented for calculating the mean and covariance of this Gaussian distribution. Based on the approximated linear Gaussian system, local information filter is derived for each sensor and the filtering estimates are fused with its neighbouring sensor nodes using the dynamic average-consensus strategy. Performance comparison of the proposed filter with the optimal centralised fusion filter is demonstrated through a multi-static manoeuvring target-tracking simulation study.

Inspec keywords: stochastic systems; Gaussian distribution; linear systems; target tracking; sensor fusion

Other keywords: Gaussian distribution; consensus-based distributed information filter; jump Markov systems; dynamic average-consensus strategy; multistatic manoeuvring target-tracking; Gaussian approximation approach; linear Gaussian system; multiple model estimation fusion

Subjects: Signal processing theory; Time-varying control systems; Other topics in statistics

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