A Robust Family of Algorithms for Adaptive Filtering Based on the Arctangent Framework

Kumar, K. Suresh; Pandey, Rajlaxmi; Bora, Sanjay Singh; George, Nithin V.

doi:10.1109/tcsii.2021.3129536

Cited by 21 publications

(4 citation statements)

References 29 publications

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“…The proposed HQC introduces a more efficient performance surface, leading to substantial improvements in dealing with impulsive noise. Krishna proposed a robust algorithm and confirmed through simulation studies that the arctan algorithm family achieves a higher performance compared to standard algorithms [72]. The filter coefficient update formulas for the algorithms are shown in Table 6.…”

Section: Other Transformation Algorithmsmentioning

confidence: 86%

Review of Advances in Active Impulsive Noise Control with Focus on Adaptive Algorithms

Liu,

Lei

2024

Applied Sciences

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Mitigating low-frequency noise in various industrial applications often involves the use of the filter-x least mean squares (FxLMS) algorithm, which relies on the mean square error criterion. This algorithm has demonstrated effectiveness in reducing noise induced by Gaussian noise within noise control systems. However, the performance of this algorithm experiences significant degradation and does not converge properly in the presence of impulsive noise. Consequently, to uphold the stability of the ANC system, several robust adaptive algorithms tailored to handle shock noise interference have been introduced. This paper systematically organizes and classifies robust adaptive algorithms designed for impulse noise based on algorithmic criteria, offering valuable insights for the research and application of pertinent active impact noise control methods.

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Section: Other Transformation Algorithmsmentioning

confidence: 86%

Review of Advances in Active Impulsive Noise Control with Focus on Adaptive Algorithms

Liu,

Lei

2024

Applied Sciences

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“…Recently, a new AP-type algorithm, named the Hybrid Affine Projection Algorithm (H-APA) [5], has been proposed, which combines the advantages of the Affine Projection Robust Mixed Norm Algorithm (APRMNA) [6] and APSA to achieve better performance under non-Gaussian noise. Of course, there are also many other methods applied to improve the robustness of adaptive filtering algorithms [7][8][9][10][11][12][13]. For satisfying the contradictory requirements between fast convergence speed and small steady-state misalignment, studies have used various methods to further improve the convergence speed and accuracy of APSA, such as various types of variable step, combined step methods [14][15][16][17][18][19][20][21], etc.…”

Section: Introductionmentioning

confidence: 99%

Variable Step Size Methods of the Hybrid Affine Projection Adaptive Filtering Algorithm under Symmetrical Non-Gaussian Noise

Zhou

Zhang

et al. 2023

Symmetry

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The idea of variable step-size was introduced into the Hybrid Affine Projection Algorithm (H-APA) and we propose two variable step size algorithms based on H-APA, which are called the Variable Step-Size Hybrid Affine Projection Algorithm (VSS-H-APA) and the Modified Variable Step-Size Hybrid Affine Projection Algorithm (MVSS-H-APA). These are two variable-step algorithms aim to further improve the robust performance and convergence speed of H-APA under non-Gaussian noise. This allows for faster convergence while maintaining stability. The MVSS-H-APA goes further than VSS-H-APA to estimate the noise in order to achieve better convergence performance. The proposed algorithm performs better than the existing algorithms in system identification under symmetric non-Gaussian noise.

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“…Moreover, the arctangent function [13][14] was demonstrated to enhance the convergence of the algorithm. Therefore, based on logistic distance metric theory [15], a new cost function is designed and the TACLDM algorithm is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Robust Maximum Correntropy Criterion Subband Adaptive Filter Algorithm for Impulsive Noise and Noisy Input

Zhao

2022

IEEE Trans. Circuits Syst. II

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Although the known maximum total generalized correntropy (MTGC) and generalized maximum blakezisserman total correntropy (GMBZTC) algorithms can maintain good performance under the errors-in-variables (EIV) model disrupted by generalized Gaussian noise, their requirement for manual adjustment of parameters is excessive, greatly increasing the practical difficulty of use. To solve this problem, the total arctangent based on logical distance metric (TACLDM) algorithm is proposed by utilizing the advantage of few parameters in logical distance metric (LDM) theory and the convergence behavior is improved by the arctangent function. Compared with other competing algorithms, the TACLDM algorithm not only has fewer parameters, but also has better robustness to generalized Gaussian noise and significantly reduces the steady-state error. Furthermore, the analysis of the algorithm in the generalized Gaussian noise environment is analyzed in detail in this paper. Finally, computer simulations demonstrate the outstanding performance of the TACLDM algorithm and the rigorous theoretical deduction in this paper.

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A Robust Family of Algorithms for Adaptive Filtering Based on the Arctangent Framework

Cited by 21 publications

References 29 publications

Review of Advances in Active Impulsive Noise Control with Focus on Adaptive Algorithms

Review of Advances in Active Impulsive Noise Control with Focus on Adaptive Algorithms

Variable Step Size Methods of the Hybrid Affine Projection Adaptive Filtering Algorithm under Symmetrical Non-Gaussian Noise

Robust Maximum Correntropy Criterion Subband Adaptive Filter Algorithm for Impulsive Noise and Noisy Input

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