Sparse representation for the ISAR image reconstruction

Mengqi Hu; John Montalbo; Shuxia Li; Ligang Sun; Zhijun G. Qiao

doi:10.1117/12.2228095

4 May 2016 Sparse representation for the ISAR image reconstruction

Mengqi Hu, John Montalbo, Shuxia Li, Ligang Sun, Zhijun G. Qiao

Proceedings Volume 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics; 98570B (2016) https://doi.org/10.1117/12.2228095
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

Abstract

In this paper, a sparse representation of the data for an inverse synthetic aperture radar (ISAR) system is provided in two dimensions. The proposed sparse representation motivates the use a of a Convex Optimization that recovers the image with far less samples, which is required by Nyquist-Shannon sampling theorem to increases the efficiency and decrease the cost of calculation in radar imaging.

Citation Download Citation

Mengqi Hu, John Montalbo, Shuxia Li, Ligang Sun, and Zhijun G. Qiao "Sparse representation for the ISAR image reconstruction", Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 98570B (4 May 2016); https://doi.org/10.1117/12.2228095

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available