Study of General Robust Subband Adaptive Filtering

Chen, Yu; Zakharov, Yuriy; He, Han; Lamare, Rodrigo C. de; Chen, Badong

doi:10.48550/arxiv.2208.08856

Cited by 1 publication

(1 citation statement)

References 89 publications

(120 reference statements)

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“…There are many methods to overcome this issue. Examples include affine projection, the recursive method [ 10 ], the subband method [ 22 ] and the data-reusing method, etc. However, relatively speaking, the complexity of the data-reusing method is usually lower.…”

Section: Introductionmentioning

confidence: 99%

Newton Recursion Based Random Data-Reusing Generalized Maximum Correntropy Criterion Adaptive Filtering Algorithm

Zhao

Qiao

et al. 2022

Entropy

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For system identification under impulsive-noise environments, the gradient-based generalized maximum correntropy criterion (GB-GMCC) algorithm can achieve a desirable filtering performance. However, the gradient method only uses the information of the first-order derivative, and the corresponding stagnation point of the method can be a maximum point, a minimum point or a saddle point, and thus the gradient method may not always be a good selection. Furthermore, GB-GMCC merely uses the current input signal to update the weight vector; facing the highly correlated input signal, the convergence rate of GB-GMCC will be dramatically damaged. To overcome these problems, based on the Newton recursion method and the data-reusing method, this paper proposes a robust adaptive filtering algorithm, which is called the Newton recursion-based data-reusing GMCC (NR-DR-GMCC). On the one hand, based on the Newton recursion method, NR-DR-GMCC can use the information of the second-order derivative to update the weight vector. On the other hand, by using the data-reusing method, our proposal uses the information of the latest M input vectors to improve the convergence performance of GB-GMCC. In addition, to further enhance the filtering performance of NR-DR-GMCC, a random strategy can be used to extract more information from the past M input vectors, and thus we obtain an enhanced NR-DR-GMCC algorithm, which is called the Newton recursion-based random data-reusing GMCC (NR-RDR-GMCC) algorithm. Compared with existing algorithms, simulation results under system identification and acoustic echo cancellation are conducted and validate that NR-RDR-GMCC can provide a better filtering performance in terms of filtering accuracy and convergence rate.

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Section: Introductionmentioning

confidence: 99%