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Distributed adaptive Kalman filter based on variational Bayesian technique
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.ORCID iD: 0000-0003-0177-1993
2019 (English)In: Control Theory and Technology, ISSN 2095-6983, Vol. 17, no 1, p. 37-47Article in journal (Refereed) Published
Abstract [en]

In this paper, distributed Kalman filter design is studied for linear dynamics with unknown measurement noise variance, which modeled by Wishart distribution. To solve the problem in a multi-agent network, a distributed adaptive Kalman filter is proposed with the help of variational Bayesian, where the posterior distribution of joint state and noise variance is approximated by a free-form distribution. The convergence of the proposed algorithm is proved in two main steps: noise statistics is estimated, where each agent only use its local information in variational Bayesian expectation (VB-E) step, and state is estimated by a consensus algorithm in variational Bayesian maximum (VB-M) step. Finally, a distributed target tracking problem is investigated with simulations for illustration.

Place, publisher, year, edition, pages
South China University of Technology , 2019. Vol. 17, no 1, p. 37-47
Keywords [en]
adaptive filter, Distributed Kalman filter, multi-agent system, variational Bayesian, Adaptive filtering, Kalman filters, Multi agent systems, Spurious signal noise, Target tracking, Adaptive kalman filter, Consensus algorithms, Distributed Kalman filters, Distributed target tracking, Multiagent networks, Posterior distributions, Wishart distribution, Adaptive filters
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-248223DOI: 10.1007/s11768-019-8183-9Scopus ID: 2-s2.0-85060635596OAI: oai:DiVA.org:kth-248223DiVA, id: diva2:1304491
Note

QC 20190412

Available from: 2019-04-12 Created: 2019-04-12 Last updated: 2019-04-12Bibliographically approved

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Hu, Xiaoming

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  • apa
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  • nn-NB
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