论文标题
空间交替的基于差异估计的稀疏贝叶斯学习，用于使用自适应拉普拉斯先验的复杂值稀疏信号恢复
Space Alternating Variational Estimation Based Sparse Bayesian Learning for Complex-value Sparse Signal Recovery Using Adaptive Laplace Priors
论文作者
论文摘要
由于其自我调节性质及其量化不确定性的能力，贝叶斯方法在广泛的稀疏信号恢复应用中取得了出色的恢复性能。但是，大多数现有方法基于真实价值信号模型，复杂值信号模型很少考虑。在本文中提出了由自适应的绝对收缩和选择操作员（Lasso）和稀疏的贝叶斯学习（SBL）框架的动机，本文提出了一种具有自适应拉普拉斯先验的分层模型，以恢复复杂的稀疏信号。此外，空间交替方法被整合到算法中，以降低所提出方法的计算复杂性。在实验中，研究了所提出的算法，用于复杂的高斯随机词典和不同类型的复杂信号。这些实验表明，所提出的算法比最先进的方法为不同类型的复杂信号提供了更好的恢复性能。
Due to its self-regularizing nature and its ability to quantify uncertainty, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal recovery applications. However, most existing methods are based on the real-value signal model, with the complex-value signal model rarely considered. Motivated by the adaptive least absolute shrinkage and selection operator (LASSO) and the sparse Bayesian learning (SBL) framework, a hierarchical model with adaptive Laplace priors is proposed in this paper for recovery of complex sparse signals. Moreover, the space alternating approach is integrated into the algorithm to reduce the computational complexity of the proposed method. In experiments, the proposed algorithm is studied for complex Gaussian random dictionaries and different types of complex signals. These experiments show that the proposed algorithm offers better recovery performance for different types of complex signals than state-of-the-art methods.
