Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)

Multi-targets ISAR Imaging Technology based on Robust Principal Component Analysis

Authors
Fan YE, Zelong WANG, Jubo ZHU
Corresponding Author
Fan YE
Available Online July 2017.
DOI
10.2991/eia-17.2017.36How to use a DOI?
Keywords
multi-targets; inverse synthetic aperture radar; low-rank; robust principal component analysis
Abstract

In multi-targets inverse synthetic aperture radar imaging, range profiles of multi-targets with different motion are coupled, so traditional Range-Doppler imaging algorithm is failure. A new imaging technology based on low-rank decomposition is proposed in this paper. After translational compensation and range compression, multi-targets signal can be decomposed into a low-rank part and a sparse part by Robust Principal Component Analysis. Then imaging processing is applied in multiple signals respectively. Simulation results verify the validity of the proposed method.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)
Series
Advances in Intelligent Systems Research
Publication Date
July 2017
ISBN
10.2991/eia-17.2017.36
ISSN
1951-6851
DOI
10.2991/eia-17.2017.36How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Fan YE
AU  - Zelong WANG
AU  - Jubo ZHU
PY  - 2017/07
DA  - 2017/07
TI  - Multi-targets ISAR Imaging Technology based on Robust Principal Component Analysis
BT  - Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)
PB  - Atlantis Press
SP  - 168
EP  - 171
SN  - 1951-6851
UR  - https://doi.org/10.2991/eia-17.2017.36
DO  - 10.2991/eia-17.2017.36
ID  - YE2017/07
ER  -