RCS Feature Extraction Using Discretized Point Scatterer with Compressive Sensing

Yeong Hoon Noh; Woobin Kim; Hyun Sung Tae; Jeong Kyu Kim; Ic Pyo Hong; Jong Gwan Yook

doi:10.1109/LAWP.2020.3042767

RCS Feature Extraction Using Discretized Point Scatterer with Compressive Sensing

Yeong Hoon Noh, Woobin Kim, Hyun Sung Tae, Jeong Kyu Kim, Ic Pyo Hong, Jong Gwan Yook

Department of Electrical and Electronic Engineering

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

4 Citations (Scopus)

Abstract

This letter presents a novel technique for radar cross section (RCS) feature extraction using discrete scattering center modeling and a basis pursuit denoising (BPDN) algorithm for compressive sensing. From the Stratton-Chu formula, a high-frequency assumption has been applied to define the target object as a combination of independent point scatterers. Using the BPDN solver, complex-valued scattering sources are determined from a matrix equation for the scattering problem. Using a numerical example, it has been verified that the proposed method can extract the RCS feature accurately, and the measurement efficiency is much higher compared to that of conventional methods.

Original language	English
Article number	9285204
Pages (from-to)	165-168
Number of pages	4
Journal	IEEE Antennas and Wireless Propagation Letters
Volume	20
Issue number	2
DOIs	https://doi.org/10.1109/LAWP.2020.3042767
Publication status	Published - 2021 Feb

Bibliographical note

Funding Information:
Manuscript received September 24, 2020; revised November 2, 2020; accepted November 28, 2020. Date of publication December 7, 2020; date of current version February 3, 2021. This work was supported by the Aerospace Low Observable Technology Laboratory Program of the Defense Acquisition Program Administration and the Agency for Defense Development of the Republic of Korea. (Corresponding author: Jong-Gwan Yook.) Yeong-Hoon Noh, Woobin Kim, and Jong-Gwan Yook are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, South Korea (e-mail: yh.noh@yonsei.ac.kr; woobink0203@yonsei.ac.kr; jgyook@yonsei.ac.kr).

Publisher Copyright:
© 2002-2011 IEEE.

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

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10.1109/LAWP.2020.3042767

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title = "RCS Feature Extraction Using Discretized Point Scatterer with Compressive Sensing",

abstract = "This letter presents a novel technique for radar cross section (RCS) feature extraction using discrete scattering center modeling and a basis pursuit denoising (BPDN) algorithm for compressive sensing. From the Stratton-Chu formula, a high-frequency assumption has been applied to define the target object as a combination of independent point scatterers. Using the BPDN solver, complex-valued scattering sources are determined from a matrix equation for the scattering problem. Using a numerical example, it has been verified that the proposed method can extract the RCS feature accurately, and the measurement efficiency is much higher compared to that of conventional methods.",

author = "Noh, {Yeong Hoon} and Woobin Kim and Tae, {Hyun Sung} and Kim, {Jeong Kyu} and Hong, {Ic Pyo} and Yook, {Jong Gwan}",

note = "Funding Information: Manuscript received September 24, 2020; revised November 2, 2020; accepted November 28, 2020. Date of publication December 7, 2020; date of current version February 3, 2021. This work was supported by the Aerospace Low Observable Technology Laboratory Program of the Defense Acquisition Program Administration and the Agency for Defense Development of the Republic of Korea. (Corresponding author: Jong-Gwan Yook.) Yeong-Hoon Noh, Woobin Kim, and Jong-Gwan Yook are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, South Korea (e-mail: yh.noh@yonsei.ac.kr; woobink0203@yonsei.ac.kr; jgyook@yonsei.ac.kr). Publisher Copyright: {\textcopyright} 2002-2011 IEEE.",

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AU - Yook, Jong Gwan

N1 - Funding Information: Manuscript received September 24, 2020; revised November 2, 2020; accepted November 28, 2020. Date of publication December 7, 2020; date of current version February 3, 2021. This work was supported by the Aerospace Low Observable Technology Laboratory Program of the Defense Acquisition Program Administration and the Agency for Defense Development of the Republic of Korea. (Corresponding author: Jong-Gwan Yook.) Yeong-Hoon Noh, Woobin Kim, and Jong-Gwan Yook are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, South Korea (e-mail: yh.noh@yonsei.ac.kr; woobink0203@yonsei.ac.kr; jgyook@yonsei.ac.kr). Publisher Copyright: © 2002-2011 IEEE.

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N2 - This letter presents a novel technique for radar cross section (RCS) feature extraction using discrete scattering center modeling and a basis pursuit denoising (BPDN) algorithm for compressive sensing. From the Stratton-Chu formula, a high-frequency assumption has been applied to define the target object as a combination of independent point scatterers. Using the BPDN solver, complex-valued scattering sources are determined from a matrix equation for the scattering problem. Using a numerical example, it has been verified that the proposed method can extract the RCS feature accurately, and the measurement efficiency is much higher compared to that of conventional methods.

AB - This letter presents a novel technique for radar cross section (RCS) feature extraction using discrete scattering center modeling and a basis pursuit denoising (BPDN) algorithm for compressive sensing. From the Stratton-Chu formula, a high-frequency assumption has been applied to define the target object as a combination of independent point scatterers. Using the BPDN solver, complex-valued scattering sources are determined from a matrix equation for the scattering problem. Using a numerical example, it has been verified that the proposed method can extract the RCS feature accurately, and the measurement efficiency is much higher compared to that of conventional methods.

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RCS Feature Extraction Using Discretized Point Scatterer with Compressive Sensing

Abstract

Bibliographical note

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