基于压缩感知的贪婪类重构算法原子识别策略综述

在压缩感知(CS)重构算法中，贪婪类算法因其硬件实现的简易性与良好的恢复精度得到了广泛研究，但算法多样化的同时出现了算法选择困难的问题。原子识别策略作为贪婪类算法的核心，其差异往往决定了算法重构性能的优劣。该文以贪婪类算法最关键的一环原子识别作为研究对象，对贪婪类重构算法的原子识别策略进行了提取与分类。根据不同策略的适用阶段和特点归纳提炼出3种一步式原子识别策略、8种进阶式原子识别策略以及3种稀疏度自适应原子识别策略。最后对原子识别策略所对应原始算法的重构性能进行了分类仿真对比。整理后的策略方便于实际应用中对算法的选择，同时为贪婪类重构算法的进一步优化提供了参考。

A Review of Atom Recognition Strategies for Greedy Class Reconstruction Algorithms Based on Compressed Sensing

Among the Compressed Sensing(CS) reconstruction algorithms, greedy algorithm has been widely studied for its simple hardware implementation and excellent recovery accuracy. However, the diversity of algorithms also presents the problem of difficult algorithm selection. As the core of greedy algorithms, the difference of atomic recognition strategy often determines its recovery performance. In this paper, atomic recognition strategy, which is the most important part of greedy algorithm, is taken as the research object. Three one-step atom recognition strategies, eight advanced atom recognition strategies and three sparsity adaptive atom recognition strategies are summarized according to the applicable stages and characteristics. Finally, the recovery performance of the original algorithms corresponding to the atomic recognition strategies are simulated and compared. The sorted strategies are convenient for the selection of algorithms in practical application, and they provide references for the further optimization of greedy algorithms.