TL;DR文章研究了利用 SFFT 算法去恢复信号在功率谱上的密度，此算法可以有效恢复高于 o (sqrt (n)) 的稀疏信号，并通过实验证明，其相比已有的波凸算法有着更出色的性能。
Abstract
We consider the problem of recovering signals from their power spectral
density. This is a classical problem referred to in literature as the phase
retrieval problem, and is of paramount importance in many fields of applied
sciences. In general, additional prior information about the signal is required
to guarantee unique recovery as the mapping from signals
本文提出了一种鲁棒且高效的压缩相位恢复方法，通过收集稀疏向量的多个线性测量值的幅值，利用约束感知向量和两阶段重建方法来重构目标信号，在随机不连贯子空间中选择感知向量后，通过低秩恢复阶段和稀疏恢复阶段的策略来准确地估算目标信号，该算法的测量数级别达到 O (k log (d/k))，在数值模拟中得到了验证。