Distributed Robust Kalman Filtering for Markov Jump Systems With Measurement Loss of Unknown Probabilities
针对测量丢失概率未知的马尔可夫跳变系统，设计了集中式和分布式鲁棒卡尔曼滤波器，并证明了分布式滤波器的估计误差有界性。
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.