Distributed Robust Kalman Filtering for Markov Jump Systems With Measurement Loss of Unknown Probabilities
Hui Li, Liping Yan, Yuanqing Xia
Abstract
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.
Topics & Concepts
Kalman filterFast Kalman filterComputer scienceInvariant extended Kalman filterControl theory (sociology)Ensemble Kalman filterMarkov processAlpha beta filterExtended Kalman filterMarkov chainBayesian probabilityMoving horizon estimationMathematicsArtificial intelligenceMachine learningStatisticsControl (management)Target Tracking and Data Fusion in Sensor NetworksDistributed Sensor Networks and Detection AlgorithmsFault Detection and Control Systems