Distributed moving horizon estimation under constraints of quantized measurements and packet dropouts

被引：0
|
作者：
Liu S. [1 ]
Zhao G. [1 ]
Zeng B. [2 ]
Gao C. [1 ]
机构：
[1] Coastal Defence Academy, Naval Aviation University, Yantai
[2] The Chinese People's Liberation Army 92095 Troop, Taizhou
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2020年 / 46卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Covariance Intersection (CI) fusion; Data quantification; Distributed moving horizon estimation; Prediction compensation; Stability analysis;
D O I：
10.13700/j.bh.1001-5965.2019.0497
中图分类号：
学科分类号：
摘要：
Aimed at the problem of network constraint, distributed state estimation for networked systems with packet dropouts and quantized measurements is studied. A group of Bernoulli distributed random variables is employed to describe the phenomenon of packet dropouts, and a prediction compensation mechanism is applied to compensate the packet dropouts. Quantized errors introduced by data quantification are described as parameter uncertainty in the observation equation, and the local estimator is obtained by solving a min-max problem in fixed time domain. The stability of the local estimator is studied, and a sufficient condition for the convergence of the expectation of the square norm of estimation error is obtained. For each local estimator, the recursive formula of the upper bound of the error covariance is derived, based on which a distributed fusion estimator is presented by using the Covariance Intersection (CI) fusion algorithm. The simulation results show that the proposed algorithm can effectively reduce the influence of packet dropouts and quantization on state estimation. © 2020, Editorial Board of JBUAA. All right reserved.
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收藏
页码：1485 / 1493
页数：8
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