Polynomial variable scaling factor improved least sum of exponentials algorithm with maximum correntropy criterion

Abstract

In this paper, a polynomial variable scaling factor improved least sum of exponentials algorithm with maximum correntropy criterion is proposed for sparse system identification. Sparse system estimation problem is increasing important topics in broadband wireless communications systems. Sparse learning algorithms for system identification achieved a better performance under Gaussian assumption, such as the zero-attracting least mean square (ZA-LMS). However, in non-Gaussian environments the existing algorithms suffer from performance degradation due to random impulsive noises. To further improve the robustness of the zero-attracting algorithms, an attempt has been made to design an improved sum of error exponentials that utilize the maximum correntropy criterion. In addition, a polynomial zero attractor is introduced to enhance the capability of sparse system identification. The test on sparse system identifications under an impulsive noise environment demonstrates that the proposed algorithm has a low steady-state misalignment compared with the others.