Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time-frequency representations. Hence, we overview recent advances dealing with time-frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time-frequency domain. Thirdly, compressive sensing based time-frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field.

中文翻译：

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压缩传感满足时频要求：稀疏信号时频处理的最新进展概述。

压缩感测是用于以次奈奎斯特速率获取稀疏信号的框架。一旦被压缩采集，许多信号就需要使用诸如时频表示之类的先进技术进行处理。因此，我们概述了使用压缩感测方法处理稀疏信号的时频处理的最新进展。本文面向信号处理从业人员，我们强调这些算法的实际方面。首先，我们简要回顾一下压缩感测的思想。其次，我们回顾了时频域中压缩感知的两种主要方法。第三，回顾了基于压缩感测的时频表示，然后描述了基于多项式傅立叶变换和短时傅立叶变换的压缩感测方法。最后，