Research Article
Tracking Performance of Improved Convex Combination Adaptive Filter Based on Maximum Correntropy Criterion
@INPROCEEDINGS{10.1007/978-3-030-06161-6_18, author={Wenjing Wu and Zhonghua Liang and Qianwen Luo and Wei Li}, title={Tracking Performance of Improved Convex Combination Adaptive Filter Based on Maximum Correntropy Criterion}, proceedings={Communications and Networking. 13th EAI International Conference, ChinaCom 2018, Chengdu, China, October 23-25, 2018, Proceedings}, proceedings_a={CHINACOM}, year={2019}, month={1}, keywords={Convex combination Maximum correntropy criterion (MCC); Non-Gaussian noise; Normalized mean square deviation (NMSD); System identification}, doi={10.1007/978-3-030-06161-6_18} }- Wenjing Wu
Zhonghua Liang
Qianwen Luo
Wei Li
Year: 2019
Tracking Performance of Improved Convex Combination Adaptive Filter Based on Maximum Correntropy Criterion
CHINACOM
Springer
DOI: 10.1007/978-3-030-06161-6_18
Abstract
A convex combination adaptive filter based on maximum correntropy criterion (CMCC) was widely used to solve the contradiction between the step size and the misadjustment in impulsive interference. However, one of the major drawbacks of the CMCC is its poor tracking ability. In order to solve this problem, this paper proposes an improved convex combination based on the maximum correntropy criterion (ICMCC), and investigates its estimation performance for system identification in the presence of non-Gaussian noise. The proposed ICMCC algorithm implements the combination of arbitrary number of maximum correntropy criterion (MCC) based adaptive filters with different adaption steps. Each MCC filter in the ICMCC is capable of tracking a specific change speed, such that the combined filter can track a variety of the change speed of weight vectors. In terms of normalized mean square deviation (NMSD) and tracking speed, the proposed algorithm shows good performance in the system identification for four non-Gaussian noise scenarios.