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.

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2022

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs14153509