Distributed adaptive Kalman filter based on variational Bayesian technique
Chen HU
Xiaoming HU
Yiguang HONG
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.
机标关键词：
资助基金：the National Natural Science Foundation of China ()the National Natural Science Foundation of China ( 61733018)the National Natural Science Foundation of China ( 61573344)
论文发表日期：2019-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：11( 37-47 )
英文信息