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Real-Time Super-Resolution ISAR Imaging Using Unsupervised Learning
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DOI：10.1109/LGRS.2021.3128525.png)
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摘要
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Compressive sensing (CS) enables high-resolution inverse synthetic aperture radar (ISAR) imaging with limited measurements. However, these methods reconstruct images via iterative optimization, resulting in a high computational load. Recently, convolutional neural networks (CNNs) have been used to perform super-resolution ISAR imaging in real time, where high-resolution images are necessarily used as ground truth. However, the desired high-resolution images are not reliable in practice. This letter presents an unsupervised CNN-based framework for super-resolution ISAR imaging. The well-trained CNN can directly produce high-resolution ISAR images in real time. Moreover, the network is trained in an unsupervised manner, which is suitable for practical applications. Furthermore, a pseudo l(0)-norm has been used as the sparse constraint for the exact image reconstruction. The proposed approach has been used to process the real ISAR data, and the experimental results are convincing.
Keyword:
Convolutional neural networks (CNNs)
inverse synthetic aperture radar (ISAR)
radar imaging
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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