Synthetic Aperture Radar Imaging using Basis Selection Compressed Sensing
Abstract
A compressed sensing method with basis selection is applied to a millimeter-wave synthetic aperture radar (SAR) imaging system. With a large candidate set of bases to choose from and without any a priori knowledge of the proper basis, the proposed method selects the sparsifying basis during the first few iterations of the L1 optimization according to the information from incomplete measurements and the coherence between the measurement matrix and sparsifying matrices. Several decision metrics can be used to select the basis, including the impulsiveness and Gini index of the available image at the current iteration. The proposed method is tested on two examples: a simulated image and its SAR measurement, and an experimental measurement obtained at 150 GHz via roaster scanning. The results from the simulation and experiment indicate that the proposed algorithm can always find a very good basis from the set of over 270 bases within the first two to five iterations of the L1 optimization.
Recommended Citation
D. Bi et al., "Synthetic Aperture Radar Imaging using Basis Selection Compressed Sensing," Circuits, Systems, and Signal Processing, vol. 34, no. 8, pp. 2561 - 2576, Birkhauser, Aug 2015.
The definitive version is available at https://doi.org/10.1007/s00034-015-9974-y
Department(s)
Electrical and Computer Engineering
Sponsor(s)
National Natural Science Foundation (China)
Specialized Research Fund for the Doctoral Program of High Education of China
Keywords and Phrases
Compressed sensing; Iterative methods; Millimeter waves; Radar; Radar signal processing; Signal reconstruction; Synthetic aperture radar; Basis selection; Best basis; Candidate sets; Incomplete measurements; L1 optimizations; Measurement matrix; Priori knowledge; Simulated images; Radar imaging; Synthetic aperture radar imaging
International Standard Serial Number (ISSN)
0278-081X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2015 Birkhauser, All rights reserved.
Publication Date
01 Aug 2015
Comments
The work of Dongjie Bi and Yongle Xie was supported by the National Natural Science Foundation of China (61371049) and the Specialized Research Fund for the Doctoral Program of High Education of China (20120185110013). The work of Yahong Rosa Zheng was supported in part by the Intelligent Systems Center of Missouri University of Science and Technology, the Inter-Discipline Inter-Campus research program of University of Missouri systems, and the Technology Research Institute of Austin.