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Enhanced Radar Imaging Using a Complex-Valued Convolutional Neural Network
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DOI：10.1109/LGRS.2018.2866567.png)
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摘要
En 中文
Convolutional neural networks (CNN) have successfully been employed to tackle several remote sensing tasks such as image classification and show better performance than previous techniques. For the radar imaging community, a natural question is: Can CNN be introduced to radar imaging and enhance its performance? This letter gives an affirmative answer to this question. We first propose a processing framework by which a complex-valued CNN (CV-CNN) is used to enhance radar imaging. Then we introduce two modifications to the CV-CNN to adapt it to radar imaging tasks. Subsequently, the method to generate training data is shown and some implementation details are presented. Finally, simulations and experiments are carried out, and both results show the superiority of the proposed method on imaging quality and computational efficiency.
Keyword:
Complex-valued convolutional neural network (CV-CNN)
radar imaging
sidelobe reduction
superresolution
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16.4
论文数:
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被引数:
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