Bayesian Filtering for High-Dimensional State-Space Models With State Partition and Error Compensation

作     者：Ke Li Shunyi Zhao Biao Huang Fei Liu Ke Li;Shunyi Zhao;Biao Huang;Fei Liu

作者机构：Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education)Institute of AutomationJiangnan UniversityWuxi 214122China Department of Chemical and Materials EngineeringUniversity of AlbertaEdmonton AB T6G 2G6Canada 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2024年第11卷第5期

页      面：1239-1249页

核心收录：

学科分类：080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：supported in part by the National Key R&D Program of China(2022YFC3401303) the Natural Science Foundation of Jiangsu Province(BK20211528) the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KFCX22_2300) 

主　　题：filtering estimation error 

摘      要：In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally *** paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear *** high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense *** that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state ***,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing *** results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.