IEEE Trans Cybern. 2022 Oct;52(10):10151-10162. doi: 10.1109/TCYB.2021.3062641. Epub 2022 Sep 19.
This article is concerned with a distributed filtering problem for Markov jump systems subject to the measurement loss with unknown probabilities. A centralized robust Kalman filter is designed by using variational Bayesian methods and a modified interacting multiple model method based on information theory (IT-IMM). Then, a distributed robust Kalman filter based on the centralized filter and a hybrid consensus method called hybrid consensus on measurement and information (HCMCI) is designed. Moreover, boundedness of the estimation errors and the estimation error covariances are studied for the distributed robust Kalman filter.
本文针对具有未知概率测量丢失的马尔可夫跳跃系统的分布式滤波问题进行了研究。利用变分贝叶斯方法和基于信息理论（IT-IMM）的改进交互多模型方法，设计了集中式鲁棒卡尔曼滤波器。然后，基于集中滤波器和一种称为测量和信息混合一致性（HCMCI）的混合一致性方法，设计了分布式鲁棒卡尔曼滤波器。此外，还研究了分布式鲁棒卡尔曼滤波器的估计误差和估计误差协方差的有界性。
