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A Sparse SAR Imaging Method Based on Multiple Measurement Vectors Model

Beijing Key Laboratory of Embedded Real-Time Information Processing Technology, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Author to whom correspondence should be addressed.
Academic Editors: Francesco Soldovieri, Raffaele Persico, Xiaofeng Li and Prasad S. Thenkabail
Remote Sens. 2017, 9(3), 297;
Received: 12 October 2016 / Revised: 2 March 2017 / Accepted: 11 March 2017 / Published: 22 March 2017
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
In recent decades, compressive sensing (CS) is a popular theory for studying the inverse problem, and has been widely used in synthetic aperture radar (SAR) image processing. However, the computation complexity of CS-based methods limits its wide applications in SAR imaging. In this paper, we propose a novel sparse SAR imaging method using the Multiple Measurement Vectors model to reduce the computation cost and enhance the imaging result. Based on using the structure information and the matched filter processing, the new CS-SAR imaging method can be applied to high-quality and high-resolution imaging under sub-Nyquist rate sampling with the advantages of saving the computational cost substantially both in time and memory. The results of simulations and real SAR data experiments suggest that the proposed method can realize SAR imaging effectively and efficiently. View Full-Text
Keywords: SAR; compressive sensing; multiple measurement vector SAR; compressive sensing; multiple measurement vector
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MDPI and ACS Style

Ao, D.; Wang, R.; Hu, C.; Li, Y. A Sparse SAR Imaging Method Based on Multiple Measurement Vectors Model. Remote Sens. 2017, 9, 297.

AMA Style

Ao D, Wang R, Hu C, Li Y. A Sparse SAR Imaging Method Based on Multiple Measurement Vectors Model. Remote Sensing. 2017; 9(3):297.

Chicago/Turabian Style

Ao, Dongyang, Rui Wang, Cheng Hu, and Yuanhao Li. 2017. "A Sparse SAR Imaging Method Based on Multiple Measurement Vectors Model" Remote Sensing 9, no. 3: 297.

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