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Single-Layer Network Realizes Sparse Aperture ISAR Imaging
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DOI：10.1109/LGRS.2024.3451305.png)
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摘要
En 中文
Inverse synthetic aperture radar (ISAR) imaging can be compromised by sparse apertures (SAs) leading to diminished image quality. While deep learning models exhibit significant potential for SAs ISAR imaging, existing methods often expand network capacity by stacking layers to enhance imaging outcomes, thereby complicating the training process. Furthermore, the adaptability of the network may be constrained by the fixed computational graph. To address this problem, we propose the ResDEQ model, a novel SAs ISAR imaging method based on a deep equilibrium framework and a single-layer ResNet. ResDEQ decouples the forward and backward propagation processes using fewer parameters, enhancing network flexibility and adaptability to various imaging requirements. Experimental results indicate that ResDEQ surpasses traditional layer-stacked networks in imaging performance.
Keyword:
Imaging
Radar imaging
Mathematical models
Computational modeling
Point cloud compression
Apertures
Backpropagation
Deep equilibrium
deep learning
inverse synthetic aperture radar (ISAR)
sparse aperture (SA) imaging
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
机构
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