基于压缩感知理论的雷达成像技术与应用研究进展

doi:10.11999/JEIT150874

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Abstract

Compressive Sensing (CS) theory, based on the sparsity of interested signal, samples degree-of-freedom of signal. CS is expected to improve the performance of imaging radar in the following aspects: improving the quality of imaging, simplifying the designing of radar hardware, shortening the imaging time and compressing data. This paper first combines the analysis of radar imaging with the three aspects of CS, namely the sparsity of interested signal, the compressive sampling and optimization method. Thereafter a particular and comprehensive review of CS theory in imaging radar is summarized, mainly including the relationship between sparsity of the scene and imaging, compressive sampling methods, fast and accurate reconstruction of the scene and the applications to different imaging radar systems. Finally, the unresolved problems in current research and further study directions are pointed out.

Key words： Compressive Sensing (CS) Radar imaging Sparse Sampling Image reconstruction

Received: 21 July 2015 Published: 04 January 2016

PACS:

TN957.52

Corresponding Authors: LI Shaodong E-mail: liying198798@126.com

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	LI Shaodong
	YANG Jun
	CHEN Wenfeng
	MA Xiaoyan

Cite this article:

LI Shaodong,YANG Jun,CHEN Wenfeng等. Overview of Radar Imaging Technique and Application Based on Compressive Sensing Theory[J]. JEIT, 2016, 38(2): 495-508.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT150874 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I2/495

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