Please use this identifier to cite or link to this item: https://doi.org/10.1109/TGRS.2010.2048575
Title: Resolution enhancement for inversed synthetic aperture radar imaging under low SNR via improved compressive sensing
Authors: Zhang, L.
Xing, M.
Qiu, C.-W. 
Li, J.
Sheng, J.
Li, Y.
Bao, Z.
Keywords: Compressing sampling
compressive sensing
inversed synthetic aperture radar (ISAR)
radar imaging
superresolution
Issue Date: Oct-2010
Source: Zhang, L., Xing, M., Qiu, C.-W., Li, J., Sheng, J., Li, Y., Bao, Z. (2010-10). Resolution enhancement for inversed synthetic aperture radar imaging under low SNR via improved compressive sensing. IEEE Transactions on Geoscience and Remote Sensing 48 (10) : 3824-3838. ScholarBank@NUS Repository. https://doi.org/10.1109/TGRS.2010.2048575
Abstract: The theory of compressed sampling (CS) indicates that exact recovery of an unknown sparse signal can be achieved from very limited samples. For inversed synthetic aperture radar (ISAR), the image of a target is usually constructed by strong scattering centers whose number is much smaller than that of pixels of an image plane. This sparsity of the ISAR signal intrinsically paves a way to apply CS to the reconstruction of high-resolution ISAR imagery. CS-based high-resolution ISAR imaging with limited pulses is developed, and it performs well in the case of high signal-to-noise ratios. However, strong noise and clutter are usually inevitable in radar imaging, which challenges current high-resolution imaging approaches based on parametric modeling, including the CS-based approach. In this paper, we present an improved version of CS-based high-resolution imaging to overcome strong noise and clutter by combining coherent projectors and weighting with the CS optimization for ISAR image generation. Real data are used to test the robustness of the improved CS imaging compared with other current techniques. Experimental results show that the approach is capable of precise estimation of scattering centers and effective suppression of noise. © 2006 IEEE.
Source Title: IEEE Transactions on Geoscience and Remote Sensing
URI: http://scholarbank.nus.edu.sg/handle/10635/57256
ISSN: 01962892
DOI: 10.1109/TGRS.2010.2048575
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