Sparsity aware normalized least mean p-power algorithms with correntropy induced metric penalty

Abstract

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.Keywords-normalized least mean p-power (NLMP); variable regularized ; correntropy induced metric (CIM);sparse parameter estimation; non-Gaussian impulsive noise.

The variable step‐sizeLMS/F algorithm using nonparametric method for adaptive system identification

“…In most cases, system noise assumed to have a Gaussian distribution is broadly accepted as it follows the central limit theorem. 1 The Gaussian noise originates due to several sources such as systems nonlinearity, time-varying thermal noise, and noise interference from the adjacent environment. Additionally, cross talk and electromagnetic interference are the other factors influencing the system.…”

“…Realistic system identification performance is affected by the presence of noise in the system. In most cases, system noise assumed to have a Gaussian distribution is broadly accepted as it follows the central limit theorem 1 . The Gaussian noise originates due to several sources such as systems nonlinearity, time‐varying thermal noise, and noise interference from the adjacent environment.…”