Compressive RCS Measurements

Guoqiang Zhao; Shiyong Li; Zhangfeng Li; Fang Liu; Houjun Sun

doi:10.1007/s00034-014-9888-0

Compressive RCS Measurements

Guoqiang Zhao^*, Shiyong Li, Zhangfeng Li, Fang Liu, Houjun Sun

^*此作品的通讯作者

集成电路与电子学院

Beijing Institute of Technology

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

A compressive radar cross section (RCS) measurement method is presented in this paper. This method relies on the theory of compressive sensing (CS). We first show that the RCS data have sparse expansions in some proper basis. According to the theory of CS, the full RCS data can be recovered from the partial measured data by convex optimization algorithms. Comparisons of the compressive measurement method and the traditional measurement method are demonstrated by means of numerical simulations as well as by real data measured in the outdoor range.

源语言	英语
页（从-至）	1379-1389
页数	11
期刊	Circuits, Systems, and Signal Processing
卷	34
期	4
DOI	https://doi.org/10.1007/s00034-014-9888-0
出版状态	已出版 - 4月 2015

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Zhao, G., Li, S., Li, Z., Liu, F., & Sun, H. (2015). Compressive RCS Measurements. Circuits, Systems, and Signal Processing, 34(4), 1379-1389. https://doi.org/10.1007/s00034-014-9888-0

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title = "Compressive RCS Measurements",

abstract = "A compressive radar cross section (RCS) measurement method is presented in this paper. This method relies on the theory of compressive sensing (CS). We first show that the RCS data have sparse expansions in some proper basis. According to the theory of CS, the full RCS data can be recovered from the partial measured data by convex optimization algorithms. Comparisons of the compressive measurement method and the traditional measurement method are demonstrated by means of numerical simulations as well as by real data measured in the outdoor range.",

keywords = "Compressive sensing (CS), Convex optimization, Radar cross section (RCS), Sparse expansion",

author = "Guoqiang Zhao and Shiyong Li and Zhangfeng Li and Fang Liu and Houjun Sun",

note = "Publisher Copyright: {\textcopyright} 2014, Springer Science+Business Media New York.",

year = "2015",

month = apr,

doi = "10.1007/s00034-014-9888-0",

language = "English",

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T1 - Compressive RCS Measurements

AU - Zhao, Guoqiang

AU - Li, Shiyong

AU - Li, Zhangfeng

AU - Liu, Fang

AU - Sun, Houjun

PY - 2015/4

Y1 - 2015/4

N2 - A compressive radar cross section (RCS) measurement method is presented in this paper. This method relies on the theory of compressive sensing (CS). We first show that the RCS data have sparse expansions in some proper basis. According to the theory of CS, the full RCS data can be recovered from the partial measured data by convex optimization algorithms. Comparisons of the compressive measurement method and the traditional measurement method are demonstrated by means of numerical simulations as well as by real data measured in the outdoor range.

AB - A compressive radar cross section (RCS) measurement method is presented in this paper. This method relies on the theory of compressive sensing (CS). We first show that the RCS data have sparse expansions in some proper basis. According to the theory of CS, the full RCS data can be recovered from the partial measured data by convex optimization algorithms. Comparisons of the compressive measurement method and the traditional measurement method are demonstrated by means of numerical simulations as well as by real data measured in the outdoor range.

KW - Compressive sensing (CS)

KW - Convex optimization

KW - Radar cross section (RCS)

KW - Sparse expansion

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U2 - 10.1007/s00034-014-9888-0

DO - 10.1007/s00034-014-9888-0

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VL - 34

SP - 1379

EP - 1389

JO - Circuits, Systems, and Signal Processing

JF - Circuits, Systems, and Signal Processing

IS - 4

ER -

Compressive RCS Measurements

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