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| DOI：https://doi.org/10.1007/s11768-019-8183-9 |
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| 基金项目:This work was supported by the National Natural Science Foundation of China (Nos. 61733018, 61573344). |
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| 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 |
