A one-bit reweighted iterative algorithm for sparse signal recovery
A one-bit reweighted iterative algorithm for sparse signal recovery
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DOI：
10.1109/icassp.2013.6638799
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发表时间：
2013-05
期刊：
影响因子：
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通讯作者：
Yanning Shen;Jun Fang;Hongbin Li;Zhi Chen
中科院分区：
文献类型：
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作者：
Yanning Shen;Jun Fang;Hongbin Li;Zhi Chen
本文考虑从一位量化测量重建稀疏或可压缩信号的问题。我们研究了一种使用对数和惩罚函数（也称为高斯熵）进行稀疏信号恢复的新方法。此外，在所提出的方法中，引入了 sigmoid 函数来量化测量的一位量化数据与重构信号之间的一致性。通过迭代最小化限制原始目标函数的凸代理函数来开发快速迭代算法。这导致了迭代重新加权过程，该过程在估计稀疏信号和细化代理函数的权重之间交替。讨论了所提出的算法和其他现有方法之间的联系。提供数值结果来说明所提出算法的有效性。
This paper considers the problem of reconstructing sparse or compressible signals from one-bit quantized measurements. We study a new method that uses a log-sum penalty function, also referred to as the Gaussian entropy, for sparse signal recovery. Additionally, in the proposed method, the sigmoid function is introduced to quantify the consistency between the measured one-bit quantized data and the reconstructed signal. A fast iterative algorithm is developed by iteratively minimizing a convex surrogate function that bounds the original objective function. This leads to an iterative reweighted process that alternates between estimating the sparse signal and refining the weights of the surrogate function. Connections between the proposed algorithm and other existing methods are discussed. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.