Evaluating the Scattering Contribution by RCS Feature Extraction Using Point Scatterer Modeling with Compressive Sensing

Yeong Hoon Noh; Woobin Kim; Hyeong Rae Im; Ic Pyo Hong; Jong Gwan Yook

doi:10.1109/AP-S/USNC-URSI47032.2022.9887321

Evaluating the Scattering Contribution by RCS Feature Extraction Using Point Scatterer Modeling with Compressive Sensing

Yeong Hoon Noh, Woobin Kim, Hyeong Rae Im, Ic Pyo Hong, Jong Gwan Yook

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper has been presented the point scatterer modeling technique to extract the radar cross section (RCS) properties for each parts of the radar object. Back-scattered electric field is formulated in matrix form by combination of the effects of scattering sources and phase correction term, and complex-valued point scatterers are computed by using iterative solver based on basis pursuit denoising (BPDN) algorithm, a kind of compressive sensing (CS). Applying the proposed method to the numerical example, simple missile model, the RCSs caused by head, leading edges, and tail wings are retrieved, and it has been verified that the proposed method can be applied to scattering factor analysis by the comparison of three components for contributions to overall RCS level.

Original language	English
Title of host publication	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1154-1155
Number of pages	2
ISBN (Electronic)	9781665496582
DOIs	https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887321
Publication status	Published - 2022
Event	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Denver, United States Duration: 2022 Jul 10 → 2022 Jul 15

Publication series

Name	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings

Conference

Conference	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022
Country/Territory	United States
City	Denver
Period	22/7/10 → 22/7/15

Bibliographical note

Funding Information:
ACKNOWLEDGEMENT 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.

Publisher Copyright:
© 2022 IEEE.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Signal Processing
Instrumentation

Access to Document

10.1109/AP-S/USNC-URSI47032.2022.9887321

Cite this

Noh, Y. H., Kim, W., Im, H. R., Hong, I. P., & Yook, J. G. (2022). Evaluating the Scattering Contribution by RCS Feature Extraction Using Point Scatterer Modeling with Compressive Sensing. In 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings (pp. 1154-1155). (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887321

Noh, Yeong Hoon ; Kim, Woobin ; Im, Hyeong Rae et al. / Evaluating the Scattering Contribution by RCS Feature Extraction Using Point Scatterer Modeling with Compressive Sensing. 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 1154-1155 (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings).

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title = "Evaluating the Scattering Contribution by RCS Feature Extraction Using Point Scatterer Modeling with Compressive Sensing",

abstract = "This paper has been presented the point scatterer modeling technique to extract the radar cross section (RCS) properties for each parts of the radar object. Back-scattered electric field is formulated in matrix form by combination of the effects of scattering sources and phase correction term, and complex-valued point scatterers are computed by using iterative solver based on basis pursuit denoising (BPDN) algorithm, a kind of compressive sensing (CS). Applying the proposed method to the numerical example, simple missile model, the RCSs caused by head, leading edges, and tail wings are retrieved, and it has been verified that the proposed method can be applied to scattering factor analysis by the comparison of three components for contributions to overall RCS level.",

author = "Noh, {Yeong Hoon} and Woobin Kim and Im, {Hyeong Rae} and Hong, {Ic Pyo} and Yook, {Jong Gwan}",

note = "Funding Information: ACKNOWLEDGEMENT 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. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 ; Conference date: 10-07-2022 Through 15-07-2022",

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series = "2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings",

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Noh, YH, Kim, W, Im, HR, Hong, IP & Yook, JG 2022, Evaluating the Scattering Contribution by RCS Feature Extraction Using Point Scatterer Modeling with Compressive Sensing. in 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1154-1155, 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022, Denver, United States, 22/7/10. https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887321

Evaluating the Scattering Contribution by RCS Feature Extraction Using Point Scatterer Modeling with Compressive Sensing. / Noh, Yeong Hoon; Kim, Woobin; Im, Hyeong Rae et al.
2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1154-1155 (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Evaluating the Scattering Contribution by RCS Feature Extraction Using Point Scatterer Modeling with Compressive Sensing

AU - Noh, Yeong Hoon

AU - Kim, Woobin

AU - Im, Hyeong Rae

AU - Hong, Ic Pyo

AU - Yook, Jong Gwan

N1 - Funding Information: ACKNOWLEDGEMENT 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. Publisher Copyright: © 2022 IEEE.

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Y1 - 2022

N2 - This paper has been presented the point scatterer modeling technique to extract the radar cross section (RCS) properties for each parts of the radar object. Back-scattered electric field is formulated in matrix form by combination of the effects of scattering sources and phase correction term, and complex-valued point scatterers are computed by using iterative solver based on basis pursuit denoising (BPDN) algorithm, a kind of compressive sensing (CS). Applying the proposed method to the numerical example, simple missile model, the RCSs caused by head, leading edges, and tail wings are retrieved, and it has been verified that the proposed method can be applied to scattering factor analysis by the comparison of three components for contributions to overall RCS level.

AB - This paper has been presented the point scatterer modeling technique to extract the radar cross section (RCS) properties for each parts of the radar object. Back-scattered electric field is formulated in matrix form by combination of the effects of scattering sources and phase correction term, and complex-valued point scatterers are computed by using iterative solver based on basis pursuit denoising (BPDN) algorithm, a kind of compressive sensing (CS). Applying the proposed method to the numerical example, simple missile model, the RCSs caused by head, leading edges, and tail wings are retrieved, and it has been verified that the proposed method can be applied to scattering factor analysis by the comparison of three components for contributions to overall RCS level.

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M3 - Conference contribution

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Y2 - 10 July 2022 through 15 July 2022

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Noh YH, Kim W, Im HR, Hong IP, Yook JG. Evaluating the Scattering Contribution by RCS Feature Extraction Using Point Scatterer Modeling with Compressive Sensing. In 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1154-1155. (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings). doi: 10.1109/AP-S/USNC-URSI47032.2022.9887321

Evaluating the Scattering Contribution by RCS Feature Extraction Using Point Scatterer Modeling with Compressive Sensing

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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