A new efficient filtered-x affine projection sign algorithm for active control of impulsive noise

Xiao, Longshuai; Wu, Ming; Jiang, Yang

doi:10.1016/j.sigpro.2015.09.015

Cited by 15 publications

(4 citation statements)

References 28 publications

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“…The parameter Pr was set to 0.001 as in [13] to match the average power of a BG process to the background noise. We evaluated the convergence performance in terms of normalized misalignment, which was used in [13], 10 log 10 E || ŵ(k) − w(k)|| 2 2 /E || ŵ(k)|| 2 2 , and ensemble averaging over 100 independent trials. We considered the fully-modeling condition where the length of the adaptive filter has the same number of taps as the unknown filter.…”

Section: Analysis Of Computational Complexity In Implementationmentioning

confidence: 99%

“…Introduction: Real-world adaptive filtering applications suffer severe impairments from background and impulsive noises, causing filter coefficient instability or divergence [1][2][3][4][5]. To mitigate impulsive interference, the sign algorithms (SA) and normalized SA (NSA) are used, which can be implemented as additional sign operations to the least-mean-square (LMS) and normalized LMS (NLMS) algorithms, respectively.…”

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confidence: 99%

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Normalized frequency‐domain block sign algorithm using ℓ1$\ell _{1}$‐norm minimization

Choi,

Chang

2024

Electronics Letters

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In this letter, a normalized frequency‐domain block sign‐error algorithm (NFBSA) is proposed by minimizing the ‐norm of the posteriori error vector for filter weight. The NFBSA updates the filter weight vector by employing the sign value of the priori error vector, which can mitigate the impact of impulsive noise. Furthermore, a variable step‐size algorithm (VSS) suitable for application in the NFBSA is proposed, which minimizes the ‐norm of the posteriori error vector with respect to the step‐size. The system identification simulations are conducted using a first‐order autoregressive signal as an input, assuming that impulsive noise occurs. The simulation results demonstrate that the NFBSA exhibits superior misalignment performance compared to the conventional time‐domain sign algorithm, and the NFBSA with the proposed VSS outperformed other VSS‐based sign algorithms.

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Section: Analysis Of Computational Complexity In Implementationmentioning

confidence: 99%

mentioning

confidence: 99%

Normalized frequency‐domain block sign algorithm using ℓ1$\ell _{1}$‐norm minimization

Choi,

Chang

2024

Electronics Letters

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“…The graphics processing units (GPUs) offer powerful parallel processing and it also has reduced computational demand in the previous studies [6,306]. Some algorithms for reducing complexity can also be integrated into the classic ANC algorithm, such as fast algorithms, set-membership filtering and PU, moreover, the PU scheme has been applied to modify the FxLMS and FxAP algorithm [199,307]. Note that the first attempt to simultaneously apply PU and setmembership approaches in ANC algorithms is presented for a NLANC system, but not for a linear system [308].…”

Section: Berkhoffmentioning

confidence: 99%

“…The above mentioned algorithms are also challenged by the difficulty of converging in the presence of impulsive noise. A simple, yet effective approach for AINC was proposed in [199], which is derived by introducing the efficient AP sign (APS) algorithm into ANC systems, resulting in the FxAPS algorithm. Additionally, two extensions regarding a VSS scheme and partial update (PU)…”

Section: ) Fxap-based Algorithms For Impulsive Noisementioning

confidence: 99%

A survey on active noise control in the past decade—Part I: Linear systems

et al. 2021

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Active noise control (ANC) is an effective way for reducing the noise level in electroacoustic or electromechanical systems. Since its first introduction in 1936, this approach has been greatly developed. This paper focuses on discussing the development of ANC techniques over the past decade. Linear ANC algorithms, including the celebrated filtered-x least-mean-square (FxLMS)based algorithms and distributed ANC algorithms, are investigated and evaluated. Nonlinear ANC (NLANC) techniques, such as functional link artificial neural network (FLANN)-based algorithms, are pursued in Part II. Furthermore, some novel methods and applications of ANC emerging in the past decade are summarized. Finally, future research challenges regarding the ANC technique are discussed.

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Review on Active Noise Control Technology for α-Stable Distribution Impulsive Noise

Chen

Liang

et al. 2021

Circuits Syst Signal Process

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A new efficient filtered-x affine projection sign algorithm for active control of impulsive noise

Cited by 15 publications

References 28 publications

Normalized frequency‐domain block sign algorithm using ℓ1$\ell _{1}$‐norm minimization

Normalized frequency‐domain block sign algorithm using ℓ1$\ell _{1}$‐norm minimization

A survey on active noise control in the past decade—Part I: Linear systems

Review on Active Noise Control Technology for α-Stable Distribution Impulsive Noise

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