Search

• To analyze the performance of three proportionate algorithms in impulsive noises. • To reveal the ranges and relations of the step-sizes in three proportionate algorithms. • To develop a decorrelation technique based variable step-size scheme to deal with the trade-off between convergence rate and steady-state error. In the literature, the proportionate least mean M-estimate (PLMM) algorithm exhibits good performance when dealing with sparse systems in the presence of impulsive noises. In this paper, the mean and mean-square behaviors of three recursion types of the PLMM algorithm are studied in depth. We derive analytically the stability, transient and steady-state results of these PLMM recursions, and find that they can achieve the same performance when properly choosing the step-size and the proportionate matrix. To improve the filter performance in both convergence rate and steady-state error, we derive a variable step-size (VSS) scheme and then present the VSS-based PLMM algorithms. In addition, based on the adaptive decorrelation strategy aiming at the colored input signals, the VSS adaptive decorrelation PLMM algorithms are developed to further speed up the convergence. Computer simulations have verified our theoretical analyses and the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Highlights • This paper provides the development of the robust censored adaptive algorithm for the impulsive noise. • This paper gives Consideration of non-Gaussian background noise as different from previous literature which only consider the Gaussian noise. • Theoretical insights into the mean and mean square performance of the CR-MCC algorithm are provided. • Simulation examples to demonstrate the performance of the proposed algorithm in impulsive noise scenarios are given. Abstract Censored observations and impulsive measurement noise are encountered in many practical applications of adaptive signal processing. Traditional adaptive filtering algorithms may fail to work in such cases. This paper proposes a robust adaptive filter algorithm predicated on maximum correntropy criteria (MCC) for censored regression. A detailed performance analysis in terms of mean and mean-square behaviour is provided. Simulations with Gaussian and non-Gaussian noise are presented to verify the theoretical results, and to demonstrate the superior performance of the proposed algorithm over existing algorithms. [ABSTRACT FROM AUTHOR]