Robust Proportionate Adaptive Filtering Algorithms Against Impulsive Interference

Abstract

The Proportionate Normalized Least Mean Square (PNLMS) algorithm, as a popular tool of signal processing, achieves excellent performance for sparse system identification. However, in previous studies, most of the cost functions used in proportionate-type sparse adaptive algorithms are based on the Mean Square Error (MSE) criterion, which is optimal only when the measurement noise is Gaussian. However, when the noise is impulsive noise, the performances of the existing proportionate-type sparse adaptive algorithms deteriorate severely. In this paper, two novel proportionate adaptive algorithms, namely, Proportionate Maximum Correntropy Criterion (PMCC) algorithm based on maximum correntropy criterion algorithm and proportionate normalized least mean square algorithm based on arctangent cost function (P-Arc-NLMS) are proposed to address this problem. The corresponding computational complexity is analyzed. The simulations are done to confirm that the proposed algorithms have advantages, compared with the existing other algorithms proposed against impulsive interference in sparse system identification.