Distributed simultaneous state-input estimation over sensor networks under quantized communication

被引:3
作者
Yu, Dongdong [2 ]
Xia, Yuanqing [1 ,2 ]
Zhai, Di -Hua [2 ]
Zhan, Yufeng [2 ]
机构
[1] Zhongyuan Univ Technol, Zhengzhou 450007, Peoples R China
[2] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
基金
中国国家自然科学基金;
关键词
Distributed state estimation; Quantized communication; Unknown inputs; Sensor networks; MINIMUM-VARIANCE INPUT; FAULT-DETECTION; KALMAN FILTER; UNKNOWN INPUT; CONSENSUS; STRATEGIES; SYSTEMS;
D O I
10.1016/j.automatica.2024.111552
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper is concerned with the distributed state estimation problem over sensor networks with a careful eye towards unknown inputs and quantized communication. Based on singular value decomposition, a unified estimator is developed to simultaneously estimate system states and unknown inputs, in which the estimator gain is determined by minimizing an upper bound on the updated error covariance. Then, a novel distributed state estimator is constructed by enforcing that each node uniformly quantizes the local estimates and the upper bounds on local error covariances before transmission. Furthermore, it is proved that the fused estimation error in each node is uniformly bounded in mean square. Finally, an illustrative example is provided to show the practical effectiveness of the proposed techniques. (c) 2024 Published by Elsevier Ltd.
引用
收藏
页数:11
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