title: Globally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications.
abstract: This paper proposes a new distributed Kalman filtering fusion with random state transition and measurement matrices, i.e., random parameter matrices Kalman filtering. It is proved that under a mild condition the fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurements; therefore, it achieves the best performance. More importantly, this result can be applied to Kalman filtering with uncertain observations including the measurement with a false alarm probability as a special case, as well as, randomly variant dynamic systems with multiple models. Numerical examples are given which support our analysis and show significant performance loss of ignoring the randomness of the parameter matrices.
authors.affiliation: Department of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, P. R. China.
authors.forename: Yingting
authors.initials: Y
authors.lastname: Luo
authors.affiliation: Department of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, P. R. China. ymzhu@scu.edu.cn.
authors.forename: Yunmin
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Department of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, P. R. China.
authors.forename: Dandan
authors.initials: D
authors.lastname: Luo
authors.affiliation: Department of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, P. R. China.
authors.forename: Jie
authors.initials: J
authors.lastname: Zhou
authors.affiliation: Department of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, P. R. China.
authors.forename: Enbin
authors.initials: E
authors.lastname: Song
authors.affiliation: Department of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, P. R. China.
authors.forename: Donghua
authors.initials: D
authors.lastname: Wang
keywords: Centralized fusion; Distributed fusion; Kalman filtering; Random parameters matrices