Inverse Synthetic Aperture Radar Imaging Using an Attention Generative Adversarial Network
نویسندگان
چکیده
The traditional inverse synthetic aperture radar (ISAR) imaging uses matched filtering and pulse accumulation methods. When improving the resolution real-time performance, there are some problems, such as high sampling rate large amount of data. Although compressed sensing (CS) method can realize high-resolution with small data, sparse reconstruction algorithm has computational complexity is time-consuming. result limited by model sparsity hypothesis. We propose a novel CS-ISAR using an attention generative adversarial network (AGAN). generator AGAN modified U-net consisting both spatial channel-wise attention. trained learn operation from down-sampling data to ISAR images. Simulations measured experiments given validate advantage proposed method.
منابع مشابه
Inverse Synthetic Aperture Radar
1The school of ITEF, The University of Queensland, Brisbane 4072, Australia 2Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy 3 School of Information Technology & Electrical Engineering, University of Queensland, Brisbane 4072, Australia 4Radar Modelling & Analysis Group, Electronic Warfare & Radar Division, Defence Science & Technology Organisation...
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14153509