Distributed Kalman Filtering Through Trace Proximity

• 成果类型: Article
• 署名作者: Liu, Wei; Shi, Peng; Wang, Shuoyu
• 署名单位: Zhejiang Gongshang University; Kochi University Technology; University of Adelaide; Kochi University Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3169956
• 发表日期: 2022
• 页码: 4908-4915
• 关键词: Kalman filters; Heuristic algorithms; Approximation algorithms; Linear systems; Weight measurement; Symmetric matrices; Noise measurement; Discrete-time linear systems; distributed Kalman filtering; sensor networks; trace; Unbiasedness

摘要

This article is concerned with the distributed Kalman filtering problem for discrete-time linear systems whose measurement information comes from a set of sensor nodes that can communicate with their direct neighbors. Two algorithms for distributed Kalman filtering are proposed, where the first algorithm is based on single-node measurement and the second algorithm is based on neighboring-node measurements. In order to improve the performance, a novel criterion is introduced to the algorithm design, where in the criterion, the proximity of matrix's trace is used to determine the proximity of positive semidefinite matrix. We prove that all the proposed algorithms for distributed Kalman filtering are unbiased. Numerical examples are provided to demonstrate the correctness of the proposed theoretical method and the performance of the proposed new design technique.