An Enhanced Set-Membership PNLMS Algorithm with a Correntropy Induced Metric Constraint for Acoustic Channel Estimation

Jin, Zuanming; Li, Yingsong; Wang, Yanyan

doi:10.3390/e19060281

Cited by 16 publications

(6 citation statements)

References 33 publications

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“…results. Using the Lemma 1 in [53], when N is large enough and the input signal is white Gaussian noise, the cost function in (22) can be modified to be…”

Section: The Proposed Acd-based MCC Algorithmsmentioning

confidence: 99%

Active Coefficient Detection Maximum Correntropy Criterion Algorithm for Sparse Channel Estimation Under Non-Gaussian Environments

Sun

Jiang

et al. 2019

IEEE Access

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In this paper, a kind of active coefficient detection (ACD)-based maximum correntropy criterion (MCC) algorithm is proposed to estimate a sparse multi-path channel under the non-Gaussian environments. The proposed ACD-based MCC algorithms are realized by developing an active coefficient detection mechanism, which can distinguish the active taps within the sparse channels and find out the position and the number of active taps. Therefore, only the active taps coefficient is updated while the trivial channel coefficients are set to be zeros. Various computer simulation experiments are carried out to investigate the performance of the proposed ACD-based MCC algorithms under different impulsive noises. The achieved simulation results prove that the proposed ACD-based MCC algorithms are effective and outperform the previous adaptive filtering algorithms for the sparse channel estimation with regard to both the convergence and the estimation error.INDEX TERMS Sparse channel estimation, maximum correntropy criterion, active coefficient detection, tap selection, impulsive noise environments.

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“…results. Using the Lemma 1 in [53], when N is large enough and the input signal is white Gaussian noise, the cost function in (22) can be modified to be…”

Section: The Proposed Acd-based MCC Algorithmsmentioning

confidence: 99%

Active Coefficient Detection Maximum Correntropy Criterion Algorithm for Sparse Channel Estimation Under Non-Gaussian Environments

Sun

Jiang

et al. 2019

IEEE Access

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“…It is worth pointing out that the main role of the proportionate scheme is to speed up the convergence, while the sparsity-aware's role is to reduce the steady-state error. In [58], [59], [60], [61], to exploit the sparsity of the underlying system as full as possible, these two strategies were combined in the NLMS algorithm. However, the reasons why the combination of these two strategies provided better results than each strategy individually are not completely clarified, and how to choose the proper sparse penalty parameter is also problem.…”

Section: Introductionmentioning

confidence: 99%

Study of Proximal Normalized Subband Adaptive Algorithm for Acoustic Echo Cancellation

Guo,

Yu,

de Lamare

et al. 2021

Preprint

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In this paper, we propose a novel normalized subband adaptive filter algorithm suited for sparse scenarios, which combines the proportionate and sparsity-aware mechanisms. The proposed algorithm is derived based on the proximal forwardbackward splitting and the soft-thresholding methods. We analyze the mean and mean square behaviors of the algorithm, which is supported by simulations. In addition, an adaptive approach for the choice of the thresholding parameter in the proximal step is also proposed based on the minimization of the mean square deviation. Simulations in the contexts of system identification and acoustic echo cancellation verify the superiority of the proposed algorithm over its counterparts.

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“…Inspired by the PNLMS, the proportionate AP (PAP) algorithm was proposed by using the idea in PNLMS to fully use the sparsity in the system via employing the data reusing principle [22]. Then, various proportionate-type AF algorithms were proposed and analyzed [23][24][25]. Moreover, a collection of zero-attraction (ZA) algorithms, such as the ZA-LMS and its reweighted form (RZA-LMS), ZA-AP, and RZA-AP algorithms, etc.…”

Section: Introductionmentioning

confidence: 99%

A Sparsity-Aware Variable Kernel Width Proportionate Affine Projection Algorithm for Identifying Sparse Systems

Jiang

Huang

et al. 2019

Symmetry

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An Enhanced Set-Membership PNLMS Algorithm with a Correntropy Induced Metric Constraint for Acoustic Channel Estimation

Cited by 16 publications

References 33 publications

Active Coefficient Detection Maximum Correntropy Criterion Algorithm for Sparse Channel Estimation Under Non-Gaussian Environments

Active Coefficient Detection Maximum Correntropy Criterion Algorithm for Sparse Channel Estimation Under Non-Gaussian Environments

Study of Proximal Normalized Subband Adaptive Algorithm for Acoustic Echo Cancellation

A Sparsity-Aware Variable Kernel Width Proportionate Affine Projection Algorithm for Identifying Sparse Systems

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