Robust non-negative least mean square algorithm based on step-size scaler against impulsive noise

Abstract

Conventional non-negative algorithms restrict the weight coefficient vector under non-negativity constraints to satisfy several inherent characteristics of a specific system. However, the presence of impulsive noise causes conventional non-negative algorithms to exhibit inferior performance. Under this background, a robust non-negative least mean square (R-NNLMS) algorithm based on a step-size scaler is proposed. The proposed algorithm uses a step-size scaler to avoid the influence of impulsive noise. For various outliers, the step-size scaler can adjust the step size of the algorithm, thereby eliminating the large error caused by impulsive noise. Furthermore, to improve the performance of the proposed algorithm in sparse system identification, the inversely-proportional R-NNLMS (IP-RNNLMS) algorithm is proposed. The simulation result demonstrates that the R-NNLMS algorithm can eliminate the influence of impulsive noise while showing fast convergence rate and low steady-state error under other noises. In addition, the IP-RNNLMS algorithm has faster convergence rate compared with the R-NNLMS algorithm under sparse system.

Selective Partial Update Adaptive Filtering Algorithms for Block-Sparse System Identification

“…e sequential block LMS (SB-LMS) algorithm and its normalized version [23] for acoustic echo cancellation problems and the sequential block NLMS (SB-NLMS) algorithm [24] were proposed to speed up the convergence rate.…”

Nonnegative Maximum Correntropy Adaptive Algorithm and Its Statistical Analysis

“…In the following, we investigate the behavior of the NNMCC algorithm when 2(b). The NNLMF algorithm proposed in [26], the R-NNLMS algorithm presented in [27] and the NNLMMN algorithm presented in [28] This assumption is reasonable as the same matrix h(p) hT (p) can be obtained from infinitely many different vectors h(p).…”

“…Hence, it is important to efficient AF algorithms to identify systems under nonnegative constraints for operation in the presence of the non-Gaussian noise. The nonnegative least mean fourth (NNLMF) algorithm, the robust non-negative least mean square algorithm (R-NNLMS) and the nonnegative least mean mix-norm (NNLMMN) algorithm have been proposed to this end [26,27,28]. Nevertheless, the performance of the NNLMF algorithm may degrade in some certain non-Gaussian noise environments, such as heavy-tailed noises.…”

Affine projection Weibull M‐transform least mean square algorithm against impulsive interference

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