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

^*Corresponding author for this work

School of Integrated Circuits and Electronics

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	1379-1389
Number of pages	11
Journal	Circuits, Systems, and Signal Processing
Volume	34
Issue number	4
DOIs	https://doi.org/10.1007/s00034-014-9888-0
Publication status	Published - Apr 2015

Keywords

Compressive sensing (CS)
Convex optimization
Radar cross section (RCS)
Sparse expansion

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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",

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AU - Li, Zhangfeng

AU - Liu, Fang

AU - Sun, Houjun

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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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JF - Circuits, Systems, and Signal Processing

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ER -

Compressive RCS Measurements

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