Downward-Looking Linear Array 3D SAR Imaging Based on Multiple Measurement Vectors Model and Continuous Compressive SensingReportar como inadecuado




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Journal of Sensors - Volume 2017 2017, Article ID 6207828, 11 pages - https:-doi.org-10.1155-2017-6207828

Research Article

Information and Navigation College, Air Force Engineering University, Xi’an 710077, China

Collaborative Innovation Center of Information Sensing and Understanding, Xi’an 710077, China

Key Laboratory for Information Science of Electromagnetic Waves, Fudan University, Shanghai 200433, China

China Satellite Maritime Tracking and Control Department, Jiangyin 214431, China

Correspondence should be addressed to Qun Zhang

Received 17 January 2017; Accepted 20 March 2017; Published 5 April 2017

Academic Editor: Stephane Evoy

Copyright © 2017 Qi-yong Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper concerns the problems of huge data and off-grid effect of cross-track direction in downward-looking linear array DLLA 3D SAR imaging. Since the 3D imaging needs a great deal of memory space, we consider the methods of downsampling to reduce the data quantity. In the azimuth direction, we proposed a method based on the multiple measurement vectors MMV model, which can enhance computational efficiency and elevate the performance of antinoise, to recover the signal. Further, in cross-track direction, since the resolution is restricted by the length of array, as well as platform size, the influence of off-grid effect is more serious than azimuth direction. Continuous compressive sensing CCS, which can solve the off-grid effect of the classical compressive sensing CS, is presented to obtain the precise imaging result under the noise scenarios. Finally, we validate our method by extension numerical experiments.





Autor: Qi-yong Liu, Qun Zhang, Fu-fei Gu, Yi-chang Chen, Le Kang, and Xiao-yu Qu

Fuente: https://www.hindawi.com/



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