Automatic Shrinkage Tuning Robust to Input Correlation for Sparsity-Aware Adaptive Filtering
Automatic Shrinkage Tuning Robust to Input Correlation for Sparsity-Aware Adaptive Filtering
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DOI：
10.1109/icassp.2018.8461994
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Kwangjin Jeong;M. Yukawa;M. Yamagishi;I. Yamada
中科院分区：
文献类型：
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作者：
Kwangjin Jeong;M. Yukawa;M. Yamagishi;I. Yamada
我们提出了一种新的自适应近端前后分裂（APFBS）算法的自动收缩调整技术。收缩调优的目的是选择一个合适的收缩参数值，使系统失配尽可能小。系统失配是基于时间平均二阶统计量来估计的。数值算例表明，在一定的信噪比（SNR）下，所提出的方法与手动选择收缩参数的彩色输入信号的性能相当接近。
We propose a novel automatic shrinkage tuning technique for the adaptive proximal forward-backward splitting (APFBS) algorithm. The shrinkage tuning aims to choose an appropriate value of the shrinkage parameter and achieve minimal system mismatch as possible. The system mismatch is approximated based on time-averaged second-order statistics. Numerical examples show that the proposed method achieves performance fairly close to that with a manually chosen shrinkage parameter for colored input signals at some signal to noise ratio (SNR).