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

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Article number9285204
Pages (from-to)165-168
Number of pages4
JournalIEEE Antennas and Wireless Propagation Letters
Volume20
Issue number2
DOIs
Publication statusPublished - 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).

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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