Robust hybrid affine projection filtering algorithm under <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si15.svg"><mml:mi>α</mml:mi></mml:math>-stable environment

Zhou, Xingli; Li, Guoliang; Wang, Ziyi; Wang, Gang; Zhang, Hongbin

doi:10.1016/j.sigpro.2023.108981

Cited by 10 publications

(1 citation statement)

References 24 publications

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“…The Affine Projection (AP) algorithm updates its filter coefficients based on the input signal and the desired signal [12]. The main equations of the AP algorithm can be expressed as follows:…”

Section: Affine Projection Algorithm (Ap)mentioning

confidence: 99%

Performance Analysis of FIR Adaptive Filter Algorithms for Denoising Adult ECG Signals: A Comparative Study

Halder¹,

Khandaker²,

Gomes³

et al. 2023

MEJAST

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This paper describes a unique study that uses multiple FIR adaptive filter algorithms to denoise adult electrocardiogram (ECG) data. The study looks at how power line interference, external electromagnetic fields, random body motions, and breathing impact ECG measurement accuracy. The article takes a fresh look at Savitzky-Golay filtering techniques by implementing and evaluating them inside the FIR adaptive filter architecture. Matlab is used to evaluate the performance of the Affine projection FIR adaptive filter (AP), Direct-form Normalized least-mean-square FIR adaptive filter (NLMS), and Sliding-window Recursive least-squares FIR adaptive filter (SWRLS). The results show how different strategies compare in terms of performance and their influence on recorded waveform quality. The study extends to our understanding of the efficiency of FIR adaptive filter algorithms in decreasing ECG signal noise and helps us better understand their potential uses in ECG signal processing. Based on reliable ECG data, the research findings assist the development of new approaches for diagnosing aberrant cardiac rhythms and examining the origins of chest discomfort. The originality of this work comes in its thorough assessment, comparison, and unique use of Savitzky-Golay filtering techniques inside FIR adaptive filter algorithms, which contributes to the area of ECG signal denoising. According to a comparative investigation, the SWRLS FIR adaptive filter method improves ECG signal denoising by 91.53% noise reduction.

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Section: Affine Projection Algorithm (Ap)mentioning

confidence: 99%

Performance Analysis of FIR Adaptive Filter Algorithms for Denoising Adult ECG Signals: A Comparative Study

Halder¹,

Khandaker²,

Gomes³

et al. 2023

MEJAST

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Maximum Total Fractional-Order Correntropy Adaptive Filtering Algorithm for Parameter Estimation Under Impulsive Noises

Yang,

Zhang,

Luo

et al. 2024

Circuits Syst Signal Process

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Adaptive Line Enhancer for Passive Sonars Based on Frequency-Domain Sparsity, Shannon Entropy Criterion and Mixed-Weighted Error

Li,

Cheng,

Qiu

2024

Arab J Sci Eng

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Adaptive line enhancer (ALE) is one of the vital signal processing techniques to the detection and recognition of underwater acoustic targets for passive sonars. Conventional ALEs, based on Gaussian noise assumption and least mean square (LMS) algorithm, can achieve good line enhancement property in Gaussian noise background. However, limited by the high steady-state misadjustment of LMS algorithm, the performance of conventional ALEs deteriorates under non-Gaussian noise background and degrades severely in processing signals with comparably lower signal-to-noise ratio (SNR). Therefore, it’s of great necessity to improve the line enhancement performances of ALE techniques to meet the demands of engineering application in passive sonars. In order to optimize the robustness and adaptability of conventional ALEs in dealing with underwater acoustic signals with much lower-SNR and in non-Gaussian noise background, a modified ALE algorithm called frequency-domain ALE based on l1-norm, Shannon entropy criterion and mixed-weighted norm (l1-SE-MWE-FALE) is proposed in this paper. The proposed l1-SE-MWE-FALE algorithm is based on the integration of frequency-domain sparsity, Shannon entropy (SE) criterion along with mixed-weighted error of LMS and least absolute deviation (LAD) to improve the ALE performance in situations above. The simulation results demonstrate that, when the input SNR is as low as – 25 dB, the local SNR (LSNR) gain for line spectrums by l1-SE-MWE-FALE is 9.8 dB, 3.7 dB and 2.3 dB higher than conventional ALE, l1-norm-based frequency-domain ALE (l1-FALE) and l1 norm-Shannon entropy criterion-based frequency-domain ALE (l1-SE-FALE), respectively. Meanwhile, the simulation results also indicate that the parameters of the proposed method can be chosen loosely and hence are insensitive to the choice of their values. Furthermore, the processing results of two different kinds of real ship-radiated noise signals recorded by passive sonars also imply the advantages of the proposed method over the other three ALEs both qualitatively and quantitatively in the respect of line spectrum LSNR gain and parameter insensitivity. The simulation and experiment results both validate the performance insensitivity to parameter adjustment and hence exhibit a good perspective of applications for passive sonars.

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Robust hybrid affine projection filtering algorithm under α-stable environment

Cited by 10 publications

References 24 publications

Performance Analysis of FIR Adaptive Filter Algorithms for Denoising Adult ECG Signals: A Comparative Study

Performance Analysis of FIR Adaptive Filter Algorithms for Denoising Adult ECG Signals: A Comparative Study

Maximum Total Fractional-Order Correntropy Adaptive Filtering Algorithm for Parameter Estimation Under Impulsive Noises

Adaptive Line Enhancer for Passive Sonars Based on Frequency-Domain Sparsity, Shannon Entropy Criterion and Mixed-Weighted Error

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