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Control Theory and Technology

, Volume 17, Issue 1, pp 37–47 | Cite as

Distributed adaptive Kalman filter based on variational Bayesian technique

  • Chen HuEmail author
  • Xiaoming Hu
  • Yiguang Hong
Article
  • 17 Downloads

Abstract

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.

Keywords

Distributed Kalman filter adaptive filter multi-agent system variational Bayesian 

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Copyright information

© Editorial Board of Control Theory & Applications, South China University of Technology and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Rocket Force University of EngineeringXi’anChina
  2. 2.Department of MathematicsRoyal Institute of Sweden (KTH)StockholmSweden
  3. 3.Institute of Systems Science and University of Chinese Academy of Sciences, Chinese Academy of SciencesBeijingChina

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