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CSAR imaging with data extrapolation and approximate GLRT techniques
Yu, L.; Zhang, Y.; Yu, L. (yljsmile@163.com)
Department微波遥感部
Source PublicationProgress In Electromagnetics Research M
2011
Volume19Pages:209-220
ISSN1937-8726
Language英语
AbstractCircular synthetic aperture radar (CSAR) is different from other usual SAR modes, e.g., Stripmap SAR or Spotlight SAR, which takes a circular path rather than a straight path. It can provide not only two-dimensional (2-D) high resolution images but also three- dimensional (3-D) information about the target. In this paper, 2- D CSAR imaging containing 3-D information about the target is discussed. Considering the limited bandwidth of radar system and the limited angular persistence of the reflector's scattering characteristic in a real scene, we combine the data extrapolation technique based on the autoregressive (AR) model with the non-coherent combination of the sub-aperture images based on the approximate Generalized Likelihood Ratio Test (GLRT) technique to get a 2-D CSAR image with resolution improved and with aspect-dependent reflectivity characteristics kept. The GTRI T-72 tank dataset is processed to test the algorithm.; Circular synthetic aperture radar (CSAR) is different from other usual SAR modes, e.g., Stripmap SAR or Spotlight SAR, which takes a circular path rather than a straight path. It can provide not only two-dimensional (2-D) high resolution images but also three- dimensional (3-D) information about the target. In this paper, 2- D CSAR imaging containing 3-D information about the target is discussed. Considering the limited bandwidth of radar system and the limited angular persistence of the reflector's scattering characteristic in a real scene, we combine the data extrapolation technique based on the autoregressive (AR) model with the non-coherent combination of the sub-aperture images based on the approximate Generalized Likelihood Ratio Test (GLRT) technique to get a 2-D CSAR image with resolution improved and with aspect-dependent reflectivity characteristics kept. The GTRI T-72 tank dataset is processed to test the algorithm.
Indexed ByEI
Funding Project中国科学院空间科学与应用研究中心
Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/3439
Collection微波遥感部
Corresponding AuthorYu, L. (yljsmile@163.com)
Recommended Citation
GB/T 7714
Yu, L.,Zhang, Y.,Yu, L. . CSAR imaging with data extrapolation and approximate GLRT techniques[J]. Progress In Electromagnetics Research M,2011,19:209-220.
APA Yu, L.,Zhang, Y.,&Yu, L. .(2011).CSAR imaging with data extrapolation and approximate GLRT techniques.Progress In Electromagnetics Research M,19,209-220.
MLA Yu, L.,et al."CSAR imaging with data extrapolation and approximate GLRT techniques".Progress In Electromagnetics Research M 19(2011):209-220.
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