An Efficient Method on ISAR Image Reconstruction via Norm Regularization

Inverse synthetic aperture radar (ISAR) imaging technology is a powerful tool to distinguish between targets with different types. The high-resolution radar image acquisition is an important basis for further automatic target recognition. In the high-frequency radar band, since the spatially collect...

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Bibliographic Details
Published in:IEEE journal on multiscale and multiphysics computational techniques 2019, Vol.4, p.290-297
Main Authors: Wu, Yu Mao, Wu, An Wen, Jin, Ya-Qiu, Li, Huanmin
Format: Article
Language:English
Subjects:
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Automatic target recognition
CLEAN
Convergence
Convolution
Deconvolution
Image acquisition
Image reconstruction
Image resolution
Image restoration
Imaging
Impulse response
Inverse synthetic aperture radar
inverse synthetic aperture radar (ISAR) image
Object recognition
Optimization
Physical optics
Radar imaging
Regularization
Sampling
scattering center (SC)
Sparsity
Target recognition
thresholding representation
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Summary:Inverse synthetic aperture radar (ISAR) imaging technology is a powerful tool to distinguish between targets with different types. The high-resolution radar image acquisition is an important basis for further automatic target recognition. In the high-frequency radar band, since the spatially collected received signals have strong sparsity in the image domain (Fourier domain), they can be downsampled and restored by corresponding norm regularization framework. When the target is electrically large, its contributions are equal to several point scatterers [also called scattering centers (SCs)]. So that the imaging process can be regarded as the summation of the point scatterers impulse response, the convolution process, and deconvolution method, the reverse process is adopted to extract the SCs from the image, where the number of SCs reflects the sparseness of ISAR image. As for the reconstruction method, while L 1 regularization is widely used, the L q (0
ISSN:2379-8815
2379-8815
DOI:10.1109/JMMCT.2019.2953880