Sparse-Aware Bias-Compensated Adaptive Filtering Algorithms Using the Maximum Correntropy Criterion for Sparse System Identification with Noisy Input

Wentao Ma; Dongqiao Zheng; Zhiyu Zhang; Jiandong Duan; Jinzhe Qiu; Xianzhi Hu

doi:10.3390/e20060407

Sparse-Aware Bias-Compensated Adaptive Filtering Algorithms Using the Maximum Correntropy Criterion for Sparse System Identification with Noisy Input

Entropy (Basel). 2018 May 25;20(6):407. doi: 10.3390/e20060407.

Authors

Wentao Ma^{1

2}, Dongqiao Zheng¹, Zhiyu Zhang¹, Jiandong Duan^{1

2}, Jinzhe Qiu¹, Xianzhi Hu³

Affiliations

¹ School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
² State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China.
³ Management Center of Internet Information, Xi'an University of Technology, Xi'an 710048, China.

Abstract

To address the sparse system identification problem under noisy input and non-Gaussian output measurement noise, two novel types of sparse bias-compensated normalized maximum correntropy criterion algorithms are developed, which are capable of eliminating the impact of non-Gaussian measurement noise and noisy input. The first is developed by using the correntropy-induced metric as the sparsity penalty constraint, which is a smoothed approximation of the ℓ 0 norm. The second is designed using the proportionate update scheme, which facilitates the close tracking of system parameter change. Simulation results confirm that the proposed algorithms can effectively improve the identification performance compared with other algorithms presented in the literature for the sparse system identification problem.

Keywords: bias-compensated; correntropy-induced metric; maximum correntropy criterion; noisy input; proportionate update; sparse system identification.