Complex multitask Bayesian compressive sensing
Complex multitask Bayesian compressive sensing
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DOI：
10.1109/icassp.2014.6854226
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发表时间：
2014-05
期刊：
影响因子：
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通讯作者：
Qisong Wu;Yimin D. Zhang;M. Amin;B. Himed
中科院分区：
文献类型：
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作者：
Qisong Wu;Yimin D. Zhang;M. Amin;B. Himed
提出了一种有效的复数多任务贝叶斯压缩感知算法(CMT-BCS)，用于稀疏或成组稀疏复数信号的恢复。现有的多任务贝叶斯压缩感知(MT-CS)算法在恢复多个实值稀疏解方面具有很强的能力。然而，有一大类传感问题涉及到复杂的值。一种简单的方法将复数值分解为独立的实部和虚部，没有考虑这两个分量的组稀疏性，因此恢复性能很差。本文首先介绍了联合处理实部和虚部的CMT-BCS算法，然后通过求解代理凸函数推导出一种快速、准确的先验参数估计算法。提出的CMT-BCS算法实现了有效的复稀疏信号恢复，性能优于MT-CS算法和复数群Lasso算法。
An effective complex multitask Bayesian compressive sensing (CMT-BCS) algorithm is proposed to recover sparse or group sparse complex signals. The existing multitask Bayesian compressive sensing (MT-CS) algorithm is powerful in recovering multiple real-valued sparse solutions. However, a large class of sensing problems deal with complex values. A simple approach, which decomposes a complex value into independent real and imaginary components, does not take into account the group sparsity of these two components and thus yields poor recovery performance. In this paper, we first introduce the CMT-BCS algorithm that jointly treats the real and imaginary components, and then derive a fast and accurate algorithm for the estimation of the prior parameters by solving a surrogate convex function. The proposed CMT-BCS algorithm achieves effective complex sparse signal recovery and outperforms MT-CS and complex group Lasso.