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

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国家科技期刊平台
NETL
NSTL
万方数据
维普

外文摘要：In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensional-ity,and directly exploiting holistic information for state inference is not always computationally affordable.This paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear systems.The high-dimensional state vector is divided into several blocks to save computation resources by avoiding the cal-culation of error covariance with immense dimensions.After that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state segmentation.More-over,the computational cost and error covariance of the pro-posed algorithm are analyzed analytically to show its distinct fea-tures compared with several existing methods.Simulation results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.

外文关键词：

Bayesian estimationerror compensationhigh-dimensional systemsstate estimationstate partition

作者：

Ke Li、Shunyi Zhao、Biao Huang、Fei Liu

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作者单位：

Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education),Institute of Automation,Jiangnan University,Wuxi 214122,China

Department of Chemical and Materials Engineering,University of Alberta,Edmonton AB T6G 2G6,Canada

基金：

国家重点研发计划江苏省自然科学基金Postgraduate Research and Practice Innovation Program of Jiangsu Province

项目编号：

2022YFC3401303BK20211528KFCX22_2300

出版年：

2024

DOI：

10.1109/JAS.2023.124137

自动化学报(英文版)

中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDEI

ISSN：2329-9266

年,卷(期)：2024.11(5)

参考文献量32