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.
Highlights
Adaptive algorithms are widely used in adaptive control, denoising, channel equalization, and system identification
The robust non-negative least mean square (R-non-negative LMS (NNLMS)) algorithm can eliminate the influence of impulsive noise. 4.3 Performance comparison This section validates the robustness of the IP-RNNLMS algorithm under sparse system
5 Conclusion In this work, the R-NNLMS algorithm based on a step-size scaler is proposed under nonnegative constraints
Summary
Adaptive algorithms are widely used in adaptive control, denoising, channel equalization, and system identification. For the family of non-negativity algorithms, the most common problem in practical application is the existence of impulsive measurement noise, which causes conventional non-negativity algorithms to have inferior performance. A robust NNLMS algorithm based on a stepsize scaler in non-negative constraint condition is proposed. The proposed algorithm uses a step-size scaler to eliminate the large estimation error caused by impulsive noise. To cope with the problem of unbalanced convergence of the R-NNLMS algorithm under sparse system, caused by the weight vector, the IP-RNNLMS algorithm using the inversely-proportional function is proposed in this paper. The impulsive noise commonly has high amplitude, which will affect the value of the output signal and cause a large error. The two curves almost coincide under the nonnegativity condition This means that the R-NNLMS algorithm can perform well under non-impulsive noise
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