Robust Sparse Normalized LMAT Algorithms for Adaptive System Identification Under Impulsive Noise Environments

Rakesh, P.; Kumar, T. K. Satish; Albu, Felix

doi:10.1007/s00034-019-01111-3

Cited by 31 publications

(13 citation statements)

References 37 publications

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“…This is due to the fact that the basic theory behind the conventional Wiener filter relies on the assumption of zero-mean and stationary signals. In order to cope with different outliers, the conventional approach should further involve additional control and robustness mechanisms, e.g., see [19] and the references therein. The investigation of such particular cases is beyond the scope of this paper.…”

Section: System Model and The Conventional Wiener Filtermentioning

confidence: 99%

“…In order to highlight these aspects, the performance of the conventional Wiener filter depending on N and for different SNRs is illustrated. Having available the statistical estimates from (63) and (64), the conventional Wiener solution is obtained based on (19). In this simulation, a very small value of the regularization parameter is used, i.e., δ = 10 −8 .…”

Section: Conventional Wiener Filter With 2 -Norm Regularizationmentioning

confidence: 99%

“…This is supported in Figure 3c,d, in which the SNR is set to 10 dB and 0 dB, respectively. As it can be observed, the largest value of δ improves the performance for any value of N. However, the 2 -norm regularization used in (19) does not exploit the sparse character of the system, which is taken into account within the 1 -norm framework presented in Section 4. Details of these results [focusing on the initial parts of the plots from Figure 3a-d] are shown in Figure 4a-d.…”

Section: Conventional Wiener Filter With 2 -Norm Regularizationmentioning

confidence: 99%

“…The Wiener filter based on the Kronecker product decomposition (KPD) presented in Section 5 involves smaller data structures with respect to the conventional Wiener filter. While the conventional solution is obtained by directly solving (19), the KPD-based Wiener filter iteratively combines the solutions from (41) and (42). In other words, instead of dealing with a single filter of length L = L 1 L 2 (as in the conventional case), the KPD approach handles two shorter filters of lengths PL 1 and PL 2 , with P L 2 .…”

Section: Wiener Filter Based On the Kronecker Product Decompositionmentioning

confidence: 99%

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An Insightful Overview of the Wiener Filter for System Identification

et al. 2021

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Efficiently solving a system identification problem represents an important step in numerous important applications. In this framework, some of the most popular solutions rely on the Wiener filter, which is widely used in practice. Moreover, it also represents a benchmark for other related optimization problems. In this paper, new insights into the regularization of the Wiener filter are provided, which is a must in real-world scenarios. A proper regularization technique is of great importance, especially in challenging conditions, e.g., when operating in noisy environments and/or when only a low quantity of data is available for the estimation of the statistics. Different regularization methods are investigated in this paper, including several new solutions that fit very well for the identification of sparse and low-rank systems. Experimental results support the theoretical developments and indicate the efficiency of the proposed techniques.

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Section: System Model and The Conventional Wiener Filtermentioning

confidence: 99%

Section: Conventional Wiener Filter With 2 -Norm Regularizationmentioning

confidence: 99%

Section: Conventional Wiener Filter With 2 -Norm Regularizationmentioning

confidence: 99%

Section: Wiener Filter Based On the Kronecker Product Decompositionmentioning

confidence: 99%

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An Insightful Overview of the Wiener Filter for System Identification

et al. 2021

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“…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%

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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The Maximum Correntropy Criterion-Based Robust Hierarchical Estimation Algorithm for Linear Parameter-Varying Systems with Non-Gaussian Noise

Wang

2022

Circuits Syst Signal Process

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Robust Sparse Normalized LMAT Algorithms for Adaptive System Identification Under Impulsive Noise Environments

Cited by 31 publications

References 37 publications

An Insightful Overview of the Wiener Filter for System Identification

An Insightful Overview of the Wiener Filter for System Identification

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

The Maximum Correntropy Criterion-Based Robust Hierarchical Estimation Algorithm for Linear Parameter-Varying Systems with Non-Gaussian Noise

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