| 王雪琼,郭静波.动态压缩感知理论研究综述[J].电子测量与仪器学报,2020,34(10):94-109 |
| 动态压缩感知理论研究综述 |
| Review of theoretical research on dynamic compressive sensing |
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| DOI： |
| 中文关键词: 动态压缩感知 稀疏重构 贝叶斯推断 最小二乘 |
| 英文关键词:dynamic compressive sensing sparsity reconstruction Bayesian inference least square algorithm. |
| 基金项目:国家自然科学基金(51677094)资助项目 |
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| 中文摘要: |
| 动态压缩感知是静态传统压缩感知向动态信号的拓展,广泛应用于医学上的磁感应成像和目标追踪等领域。 由于工程
中的动态信号在某一转换基下具有随时间缓慢变化的稀疏特性,因而可以运用欠定的测量矩阵对其进行压缩。 动态压缩感知
理论主要包括动态信号的稀疏表示、动态压缩测量过程和动态信号的重构 3 个方面的研究内容。 全面综述动态压缩感知的基
本概念,归纳总结现有动态压缩感知理论中对动态信号的建模方法;对已有的动态信号重构算法进行了归类,并详述了各类算
法的计算思路;最后介绍了动态压缩感知的典型应用,并对动态压缩感知信号重构算法的研究前景进行了探讨。 |
| 英文摘要: |
| Dynamic compressive sensing is an extension of traditional static compressive sensing to dynamic signals, which has a wide
application in MRI, video compressive sensing and target tracking. Since dynamic signals are usually sparse in some transformed
matrices and change slowly with time varying, an underdetermined measurement matrix can be used to compress the signals. The
research of dynamic compressive sensing mainly focuses on three parts: Sparse representation of dynamic signals, dynamic compressive
measurement, and reconstruction of dynamic signals. A comprehensive survey about dynamic compressive sensing is given in this article.
At first, the basic concept of dynamic compressed sensing is introduced, which includes several mathematic models of dynamic signals,
sparse dictionary learning algorithms and methods of adaptive measurement. Secondly, we classify the reconstruction algorithms into two
main parts: Least square based algorithms and Bayesian algorithms, and we also introduce some representative algorithms in detail from
each part. Finally, several applications of dynamic compressed sensing are introduced, and we provide a reference for further
investigation on reconstruction algorithms. |
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