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Distributed adaptive Kalman filter based on variational Bayesian technique
ChenHU,XiaomingHU,YiguangHONG
0
(Rocket Force University of Engineering, Xi’an Shannxi 710025, China)
摘要:
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.
关键词:  Distributed Kalman filter, adaptive filter, multi-agent system, variational Bayesian
DOI:
基金项目:This work was supported by the National Natural Science Foundation of China (Nos. 61733018, 61573344).
Distributed adaptive Kalman filter based on variational Bayesian technique
Chen HU,Xiaoming HU,Yiguang HONG
(Rocket Force University of Engineering, Xi’an Shannxi 710025, China;Department of Mathematics, Royal Institute of Sweden (KTH), Sweden;Institute of Systems Science and University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China)
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.
Key words:  Distributed Kalman filter, adaptive filter, multi-agent system, variational Bayesian