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 in | IEEE transactions on systems, man, and cybernetics. Systems Vol. 55; no. 1; pp. 674 - 684 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.01.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2168-2216 2168-2232 |
| DOI | 10.1109/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. |
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| 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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