Sparsity Aware Normalized Least Mean p-power Algorithms with Correntropy Induced Metric Penalty

TL;DR

This paper introduces sparsity-aware normalized least mean p-power algorithms with correntropy-induced metric penalties to improve system identification in impulsive noise environments, enhancing stability and convergence.

Contribution

It proposes a novel sparse NLMP algorithm using CIM penalty and an improved version with variable regularization for better performance.

Findings

The algorithms effectively identify non-Gaussian impulsive noise systems.

The proposed methods outperform existing algorithms in convergence speed.

Numerical simulations validate the effectiveness of the algorithms.

Abstract

For identifying the non-Gaussian impulsive noise systems, normalized LMP (NLMP) has been proposed to combat impulsive-inducing instability. However, the standard algorithm is without considering the inherent sparse structure distribution of unknown system. To exploit sparsity as well as to mitigate the impulsive noise, this paper proposes a sparse NLMP algorithm, i.e., Correntropy Induced Metric (CIM) constraint based NLMP (CIMNLMP). Based on the first proposed algorithm, moreover, we propose an improved CIM constraint variable regularized NLMP(CIMVRNLMP) algorithm by utilizing variable regularized parameter(VRP) selection method which can further adjust convergence speed and steady-state error. Numerical simulations are given to confirm the proposed algorithms.