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Variable regularization for normalized subband adaptive filter
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DOI：10.1016/j.sigpro.2014.04.036.png)
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To overcome the performance degradation of least mean square (LMS)-type algorithms when input signals are correlated, the normalized subband adaptive filter (NSAF) was developed. In the NSAF, the regularization parameter influences the stability and performance. In addition, there is a trade-off between convergence rate and steady-state mean square deviation (MSD) according to the change of the parameter. Therefore, to achieve both fast convergence rate and low steady-state MSD, the parameter should be varied. In this paper, a variable regularization scheme for the NSAF is derived on the basis of the orthogonality between the weight-error vector and weight vector update, and by using the calculated MSD. The performance of the variable regularization algorithm is evaluated in terms of MSD. Our simulation results exhibit fast convergence and low steady-state MSD when using the proposed algorithm. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Adaptive filters
Variable regularization
Normalized subband adaptive filter (NSAF)
Mean-square deviation (MSD)
期刊
IF:
3.6
论文数:
9.9K
被引数:
1.7W
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Steady-state mean-square error analysis of regularized normalized subband adaptive filters正则化归一化子带自适应滤波器的稳态均方误差分析.png)
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