Shrinkage tuning based on an unbiased MSE estimate for sparsity-aware adaptive filtering
Shrinkage tuning based on an unbiased MSE estimate for sparsity-aware adaptive filtering
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DOI：
10.1109/icassp.2014.6854650
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发表时间：
2014-05
期刊：
影响因子：
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通讯作者：
M. Yamagishi;M. Yukawa;I. Yamada
中科院分区：
文献类型：
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作者：
M. Yamagishi;M. Yukawa;I. Yamada
有效利用待估计系统的稀疏性是获得良好自适应滤波性能的关键。这可以通过精心选择参数的自适应近端前向后分裂（APFBS）来实现。在本文中，我们提出了一种基于无偏MSE估计的最小化原理的系统参数调谐。由于所提出的均方差估计采用分段二次型结构，可以在较低的计算负荷下得到最小值。数值算例表明，该方法在更宽信噪比范围内的性能优于APFBS的启发式参数整定。
Effective utilization of sparsity of the system to be estimated is a key to achieve excellent adaptive filtering performances. This can be realized by the adaptive proximal forward-backward splitting (APFBS) with carefully chosen parameters. In this paper, we propose a systematic parameter tuning based on a minimization principle of an unbiased MSE estimate. Thanks to the piecewise quadratic structure of the proposed MSE estimate, we can obtain its minimizer with low computational load. A numerical example demonstrates the efficacy of the proposed parameter tuning by its excellent performance over a broader range of SNR than a heuristic parameter tuning of the APFBS.