Constrained maximum correntropy adaptive filtering

被引:68
作者
Peng, Siyuan [1 ]
Chen, Badong [2 ]
Sun, Lei [3 ]
Ser, Wee [1 ]
Lin, Zhiping [1 ]
机构
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
[2] Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Peoples R China
[3] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China
基金
中国国家自然科学基金;
关键词
Constrained adaptive filtering; Maximum correntropy criterion; Non-Gaussian signal processing; Convergence analysis; SQUARE ERROR ANALYSIS; ALGORITHM; ORDER;
D O I
10.1016/j.sigpro.2017.05.009
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Constrained adaptive filtering algorithms have been extensively studied in many applications. Most existing constrained adaptive filtering algorithms are developed under the mean square error (MSE) criterion, which is an ideal optimality criterion under Gaussian noises. This assumption however fails to model the behavior of non-Gaussian noises found in practice. Motivated by the robustness and simplicity of maximum correntropy criterion (MCC) for non-Gaussian impulsive noises, this paper proposes a new adaptive filtering algorithm called constrained maximum correntropy criterion (CMCC). Specifically, CMCC incorporates a linear constraint into a MCC filter to solve a constrained optimization problem explicitly. The proposed adaptive filtering algorithm is easy to implement, has low computational complexity, and can significantly outperform those MSE based constrained adaptive algorithms in heavy-tailed impulsive noises. Additionally, the mean square convergence behaviors are studied under energy conservation relation, and a sufficient condition to ensure the mean square convergence and the steady-state mean square deviation (MSD) of the CMCC algorithm are obtained. Simulation results confirm the theoretical predictions under both Gaussian and non-Gaussian noises, and demonstrate the excellent performance of the novel algorithm by comparing it with other conventional methods. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:116 / 126
页数:11
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