Analysis of super-resolution radar imaging based on sparse regularization

Xiaoxiang Zhu; Guanghu Jin; Feng He; Zhen Dong; Guozhong Chen; Di Zhao

doi:10.1109/IGARSS.2016.7729265

Analysis of super-resolution radar imaging based on sparse regularization

Xiaoxiang Zhu
, Guanghu Jin
, Feng He
, Zhen Dong
, Guozhong Chen
, Di Zhao

National University of Defense Technology (NUDT)
Shanghai Institute of Satellite Engineering

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

3 Scopus citations

Abstract

For sparse signals (direct or indirect), sparse imaging methods can break through limitations of the conventional SAR methods. In this paper, we introduce the basic theory of sparse representation and reconstruction, and then implements several imaging algorithms including FFT and sparse methods. Through comparison, we conclude a good sparse reconstruction algorithm in SAR imaging. Besides, a new strategy of finding a better regularization parameter in sparse reconstruction is implemented. The imaging result of us has a higher resolution and much lower side lobe than the conventional algorithm based on matched filter theory.

Original language	English
Title of host publication	2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1046-1049
Number of pages	4
ISBN (Electronic)	9781509033324
DOIs	https://doi.org/10.1109/IGARSS.2016.7729265
State	Published - 1 Nov 2016
Externally published	Yes
Event	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China Duration: 10 Jul 2016 → 15 Jul 2016

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2016-November

Conference

Conference	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Country/Territory	China
City	Beijing
Period	10/07/16 → 15/07/16

Keywords

reconstruction
regularization parameter
SAR
sparse

Access to Document

10.1109/IGARSS.2016.7729265

Cite this

Zhu, X., Jin, G., He, F., Dong, Z., Chen, G., & Zhao, D. (2016). Analysis of super-resolution radar imaging based on sparse regularization. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings (pp. 1046-1049). Article 7729265 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2016.7729265

@inproceedings{ac0c7015a02e4b899ae4ec98846c47bb,

title = "Analysis of super-resolution radar imaging based on sparse regularization",

abstract = "For sparse signals (direct or indirect), sparse imaging methods can break through limitations of the conventional SAR methods. In this paper, we introduce the basic theory of sparse representation and reconstruction, and then implements several imaging algorithms including FFT and sparse methods. Through comparison, we conclude a good sparse reconstruction algorithm in SAR imaging. Besides, a new strategy of finding a better regularization parameter in sparse reconstruction is implemented. The imaging result of us has a higher resolution and much lower side lobe than the conventional algorithm based on matched filter theory.",

keywords = "reconstruction, regularization parameter, SAR, sparse",

author = "Xiaoxiang Zhu and Guanghu Jin and Feng He and Zhen Dong and Guozhong Chen and Di Zhao",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",

year = "2016",

month = nov,

day = "1",

doi = "10.1109/IGARSS.2016.7729265",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1046--1049",

booktitle = "2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings",

}

Zhu, X, Jin, G, He, F, Dong, Z, Chen, G & Zhao, D 2016, Analysis of super-resolution radar imaging based on sparse regularization. in 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings., 7729265, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2016-November, Institute of Electrical and Electronics Engineers Inc., pp. 1046-1049, 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, China, 10/07/16. https://doi.org/10.1109/IGARSS.2016.7729265

Analysis of super-resolution radar imaging based on sparse regularization. / Zhu, Xiaoxiang; Jin, Guanghu; He, Feng et al.
2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1046-1049 7729265 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November).

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

TY - GEN

T1 - Analysis of super-resolution radar imaging based on sparse regularization

AU - Zhu, Xiaoxiang

AU - Jin, Guanghu

AU - He, Feng

AU - Dong, Zhen

AU - Chen, Guozhong

AU - Zhao, Di

PY - 2016/11/1

Y1 - 2016/11/1

N2 - For sparse signals (direct or indirect), sparse imaging methods can break through limitations of the conventional SAR methods. In this paper, we introduce the basic theory of sparse representation and reconstruction, and then implements several imaging algorithms including FFT and sparse methods. Through comparison, we conclude a good sparse reconstruction algorithm in SAR imaging. Besides, a new strategy of finding a better regularization parameter in sparse reconstruction is implemented. The imaging result of us has a higher resolution and much lower side lobe than the conventional algorithm based on matched filter theory.

AB - For sparse signals (direct or indirect), sparse imaging methods can break through limitations of the conventional SAR methods. In this paper, we introduce the basic theory of sparse representation and reconstruction, and then implements several imaging algorithms including FFT and sparse methods. Through comparison, we conclude a good sparse reconstruction algorithm in SAR imaging. Besides, a new strategy of finding a better regularization parameter in sparse reconstruction is implemented. The imaging result of us has a higher resolution and much lower side lobe than the conventional algorithm based on matched filter theory.

KW - reconstruction

KW - regularization parameter

KW - SAR

KW - sparse

UR - https://www.scopus.com/pages/publications/85007453230

U2 - 10.1109/IGARSS.2016.7729265

DO - 10.1109/IGARSS.2016.7729265

M3 - Conference contribution

AN - SCOPUS:85007453230

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 1046

EP - 1049

BT - 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016

Y2 - 10 July 2016 through 15 July 2016

ER -

Zhu X, Jin G, He F, Dong Z, Chen G, Zhao D. Analysis of super-resolution radar imaging based on sparse regularization. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1046-1049. 7729265. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2016.7729265

Analysis of super-resolution radar imaging based on sparse regularization

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this