Robust Recursive Filtering for Stochastic Systems With Time-Correlated Fading Channels

被引:32
|
作者
Tan, Hailong [1 ,2 ]
Shen, Bo [2 ,3 ]
Shu, Huisheng [4 ]
机构
[1] Anhui Polytech Univ, Sch Math Phys & Finance, Wuhu 241000, Peoples R China
[2] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China
[3] Donghua Univ, Engn Res Ctr Digitized Text & Fash Technol, Minist Educ, Shanghai 201620, Peoples R China
[4] Donghua Univ, Coll Sci, Shanghai 201620, Peoples R China
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2022年 / 52卷 / 05期
基金
中国国家自然科学基金; 上海市自然科学基金;
关键词
Fading channels; Uncertain systems; Radio frequency; Kalman filters; Upper bound; Noise measurement; Forward error correction; filtering; recursive estimation; robustness; uncertain systems; VARYING SYSTEMS; DESIGN;
D O I
10.1109/TSMC.2021.3062848
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 article is concerned with the robust recursive filtering (RF) problem for a class of stochastic uncertain systems subject to time-correlated fading channels. The measurement received by the sensor is transmitted to the remote filter through the time-correlated fading channel where the channel coefficient evolves according to a certain dynamics and hence exhibits a time-correlated nature. The parameter uncertainties of the system are described by norm-bounded unknown matrices. By introducing a class of auxiliary variables, an augmented system is constructed to reflect the dynamics of the fading coefficient and state simultaneously. Then, a recursive filter is designed which is capable of online computation. Furthermore, an upper bound is guaranteed for the filtering error covariance (FEC) for the possible parameter uncertainties as well as the time-correlated fading channels. With the help of the completing-the-squares technique, filter gains are parameterized by minimizing the obtained upper bound. Finally, two examples are employed to verify the effectiveness of the proposed robust RF method.
引用
收藏
页码:3102 / 3112
页数:11
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