Robust Bias-Compensated CR-NSAF Algorithm: Design and Performance Analysis

The censored regression (CR)-based normalized subband adaptive algorithm (CR-NSAF) model has been recently introduced for processing signals with censored data. However, the effectiveness of this algorithm declines when dealing with noisy input signals in impulsive noise environments. To resolve thi...

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Published inIEEE transactions on systems, man, and cybernetics. Systems Vol. 55; no. 1; pp. 674 - 684
Main Authors Wen, Pengwei, Wang, Bolin, Qu, Boyang, Zhang, Sheng, Zhao, Haiquan, Liang, Jing
Format Journal Article
LanguageEnglish
Published IEEE 01.01.2025
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ISSN2168-2216
2168-2232
DOI10.1109/TSMC.2024.3491188

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Abstract The censored regression (CR)-based normalized subband adaptive algorithm (CR-NSAF) model has been recently introduced for processing signals with censored data. However, the effectiveness of this algorithm declines when dealing with noisy input signals in impulsive noise environments. To resolve this challenge, we propose a robust bias-compensated CR-NSAF algorithm (RBC-CRNSAF). This algorithm alleviates the negative impacts of the CR system and improves robustness by employing a logarithmic cost function approach. It also minimizes estimation bias from input noise by incorporating new compensation terms into the weights update function. Additionally, we analyze the computational complexity, convergence characteristics, and stability conditions of the algorithm. Finally, computer simulations indicate that RBC-CRNSAF considerably outperforms other similar algorithms in impulsive noise environments, validating its enhanced performance.
AbstractList The censored regression (CR)-based normalized subband adaptive algorithm (CR-NSAF) model has been recently introduced for processing signals with censored data. However, the effectiveness of this algorithm declines when dealing with noisy input signals in impulsive noise environments. To resolve this challenge, we propose a robust bias-compensated CR-NSAF algorithm (RBC-CRNSAF). This algorithm alleviates the negative impacts of the CR system and improves robustness by employing a logarithmic cost function approach. It also minimizes estimation bias from input noise by incorporating new compensation terms into the weights update function. Additionally, we analyze the computational complexity, convergence characteristics, and stability conditions of the algorithm. Finally, computer simulations indicate that RBC-CRNSAF considerably outperforms other similar algorithms in impulsive noise environments, validating its enhanced performance.
Author Zhang, Sheng
Liang, Jing
Wen, Pengwei
Qu, Boyang
Wang, Bolin
Zhao, Haiquan
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Snippet The censored regression (CR)-based normalized subband adaptive algorithm (CR-NSAF) model has been recently introduced for processing signals with censored...
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SubjectTerms Bias-compensated
censored regression (CR)
Convergence
Cost function
Data models
Estimation
logarithmic function
Noise
Noise measurement
normalized subband adaptive filtering (NSAF)
Robustness
Signal processing algorithms
Steady-state
Vectors
Title Robust Bias-Compensated CR-NSAF Algorithm: Design and Performance Analysis
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