论文标题
最佳分析和用于复杂压缩传感的稀疏算法
Optimality Analysis and Block Sparse Algorithm for Complex Compressed Sensing
论文作者
论文摘要
最近，出现了许多新的压缩传感挑战（CS），例如块稀疏性。在本文中，我们提出了一种用于求解复杂场中具有稀疏约束（BSC）的CS的新算法。首先，基于块稀疏特性，我们提出了一个新模型，以与BSC处理CS并分析该模型中涉及的功能的属性。然后，我们提出一个新的$τ$定位点，并分析相应的一阶足够和必要的条件。这样可以确保我们进一步开发牛顿硬质势率追求（BNHTP）算法，以有效地使用BSC解决CS。最后，初步数值实验表明，与经典AMP算法相比，BNHTP算法在恢复精度和计算时间方面具有较高的性能。
Recently, many new challenges in Compressed Sensing (CS), such as block sparsity, arose. In this paper, we present a new algorithm for solving CS with block sparse constraints (BSC) in complex fields. Firstly, based on block sparsity characteristics, we propose a new model to deal with CS with BSC and analyze the properties of the functions involved in this model. We then present a new $τ$-stationary point and analyze corresponding first-order sufficient and necessary conditions. That ensures we to further develop a block Newton hard-thresholding pursuit (BNHTP) algorithm for efficiently solving CS with BSC. Finally, preliminary numerical experiments demonstrate that the BNHTP algorithm has superior performance in terms of recovery accuracy and calculation time when compared with the classical AMP algorithm.
