Robust Normalized Least Mean Absolute Third Algorithms

Xiong, Kui; Wang, Shiyuan; Chen, Badong

doi:10.1109/access.2019.2891549

Cited by 21 publications

(9 citation statements)

References 40 publications

(87 reference statements)

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“…Further the matrix Ξ k influences the convergence rate, because it is positive definite under the convergence condition (30). According to (27) and (29), the fast convergence speed occurs when the quadratic functions β 1 and β 2 on the step size µ are minimum. Taking the derivative of β 1 and β 2 with respect to µ, we further obtain the optimal µ for the fast convergence:…”

Section: B Mean-square Behaviormentioning

confidence: 99%

“…The correntropy function can significantly compress the data with large amplitude, the maximum correntropy criterion (MCC) was frequently used for designing robust LMS-like algorithms [23]- [25], while this requires properly choosing the kernel width parameter. By inserting an upper bound on the squared error into the weights update to suppress the impulsive noise, the normalized least mean absolute third algorithm and its improvements were proposed [26], [27]. Similar to NLMS, these robust algorithms also have no decorrelation capability for correlated input signals.…”

Section: Introductionmentioning

confidence: 99%

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M-Estimate Based Normalized Subband Adaptive Filter Algorithm: Performance Analysis and Improvements

Chen

et al. 2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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This paper studies the mean and mean-square behaviors of the M-estimate based normalized subband adaptive filter algorithm (M-NSAF) with robustness against impulsive noise. Based on the contaminated-Gaussian noise model, the stability condition, transient and steady-state results for the algorithm are formulated analytically. The analysis results help us to better understand the M-NSAF performance in impulsive noise. To further obtain fast convergence and low steady-state estimation error, we derive a variable step size (VSS) M-NSAF algorithm. This VSS scheme is also generalized to proportionate M-NSAF variant for sparse systems. Computer simulations on system identification in impulsive noise and acoustic echo cancellation with double-talk are performed to demonstrate our theoretical analysis and the effectiveness of the proposed algorithms.

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Section: B Mean-square Behaviormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

M-Estimate Based Normalized Subband Adaptive Filter Algorithm: Performance Analysis and Improvements

Chen

et al. 2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…The maximum correntropy criterion (MCC) algorithm was presented and successfully applied to the adaptive filter for impulsive environment noise [10,11]. A novel robust normalized least mean absolute third (RNLMAT) algorithm has been proposed by using the third-order in the estimation error as the normalization [12], and it provides good robustness and filtering accuracy in impulsive noises. Some researchers used the cost function to suppress the performance degradation caused by excessive iteration error.…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Filtering Algorithm for Self-Interference Cancellation with Impulsive Noise

Zhang

Shi

et al. 2021

Electronics

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Self-interference (SI) is usually generated by the simultaneous transmission and reception in the same system, and the variable SI channel and impulsive noise make it difficult to eliminate. Therefore, this paper proposes an adaptive digital SI cancellation algorithm, which is an improved normalized sub-band adaptive filtering (NSAF) algorithm based on the sparsity of the SI channel and the arctangent cost function. The weight vector is hardly updated when the impulsive noise occurs, and the iteration error resulting from impulsive noise is significantly reduced. Another major factor affecting the performance of SI cancellation is the variable SI channel. To solve this problem, the sparsity of the SI channel is estimated with the estimation of the weight vector at each iteration, and it is used to adjust the weight vector. Then, the convergence performance and calculation complexity are analyzed theoretically. Simulation results indicate that the proposed algorithm has better performance than the referenced algorithms.

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“…Least mean square (LMS) algorithm which is simple and low computation has been widely used [15]. Most researchers have modified accurately on LMS and NLMS algorithms [16] and [17], the variable step-size LMS [18], the sign mechanism with NLMS [2] and so on.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis and Enhancement of Spline Adaptive Filtering based on Adaptive Step-size Variable Leaky Least Mean Square Algorithm

Prongnuch¹,

Sitjongsataporn²

2020

Adv. sci. technol. eng. syst. j.

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This paper presents an adaptive step-size and variable leaky least mean square algorithm based on nonlinear adaptive filter with the adaptive lookup table using spline interpolation. An adaptive step-size approach is proposed with the energy of squared previous and present errors to boost up the convergence rate. A modified variable leaky mechanism is proposed with the optimal leaky parameter by using the recursion form. The proposed algorithm merges an adaptive step-size and a modified variable leaky method with least mean square algorithm for linear and nonlinear network part of spline adaptive filtering in term of fast convergence enhancement. Experimental results demonstrate that proposed algorithm can notably achieve a competitive performance on the convergence rate in comparison with the conventional least mean square algorithm for spline adaptive filter. Simulation results suggest that mean square error performance of proposed algorithm can be partially assessed using adaptive step-size with the variable leaky parameters indicating better than the conventional least mean square algorithm by 16.76%.

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Robust Normalized Least Mean Absolute Third Algorithms

Cited by 21 publications

References 40 publications

M-Estimate Based Normalized Subband Adaptive Filter Algorithm: Performance Analysis and Improvements

M-Estimate Based Normalized Subband Adaptive Filter Algorithm: Performance Analysis and Improvements

Robust Adaptive Filtering Algorithm for Self-Interference Cancellation with Impulsive Noise

Performance Analysis and Enhancement of Spline Adaptive Filtering based on Adaptive Step-size Variable Leaky Least Mean Square Algorithm

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