DocumentCode :
142513
Title :
CS-based high-resolution ISAR imaging with adaptive sparse basis
Author :
Linna Pang ; Shunsheng Zhang ; Chan Liu ; Xiaozhen Tian
Author_Institution :
Res. Inst. of Electron. Sci. & Technol., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
fYear :
2014
fDate :
13-18 July 2014
Firstpage :
362
Lastpage :
365
Abstract :
The theory of compressed sensing (CS) indicates that the optimal reconstruction of an unknown ISAR signal with intrinsical sparsity can be achieved by solving a sparsity-driven optimization problem. Due to this property, a novel method to construct sparse basis is presented for ISAR image generation. We utilize discrete chirp fourier transform (dcft) and CLEAN to decompose the signal of interest into multiple LFM or chirp signals. The proposed method can generate adaptive sparse basis according to the frequencies and chirp rates of these chirp signals. Our proposed method which uses much fewer measured data can get almost the same image compared with the uncompressed conventional imaging algorithm and our proposal outperforms the CS-based method with standard Fourier basis. Both simulated and real experimental results can demonstrate the effectiveness and feasibility of the method.
Keywords :
compressed sensing; geophysical image processing; geophysical techniques; image reconstruction; remote sensing by radar; synthetic aperture radar; CS-based high-resolution ISAR imaging; ISAR image generation; ISAR signal reconstruction; adaptive sparse basis; chirp signals; compressed sensing theory; discrete chirp Fourier transform; sparsity-driven optimization problem; standard Fourier basis; uncompressed conventional imaging algorithm; Azimuth; Chirp; Compressed sensing; Image resolution; Imaging; Radar imaging; Standards;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International
Conference_Location :
Quebec City, QC
Type :
conf
DOI :
10.1109/IGARSS.2014.6946433
Filename :
6946433
Link To Document :
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