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首页> 外文会议>International Conference on Signal Image Processing and Communication >Radar jamming classification and recognition technology based on deep learning
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Radar jamming classification and recognition technology based on deep learning

机译：基于深度学习的雷达干扰分类与识别技术

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摘要

Towed radar active jamming has attracted much attention in the military struggle of air-to-air combat because of its simple structure and remarkable efficiency. The radar jamming patterns are becoming more and more complicated. In order to realize the identification of towed decoys, it is necessary to classify and identify the jamming patterns. The difference between time-frequency images of different interference patterns is the key to classification and recognition. Deep learning provides classifiers for classification algorithms with its powerful image data processing capabilities. Therefore, in this paper, aiming at towed decoy interference, the convolutional neural network, which is good at image analysis in deep learning, is applied to the radar active interference pattern time-frequency image classification and recognition technology. The simulation experiment part uses convolutional neural network (ResNeXt residual network) to classify and verify two different interference patterns of dense false target interference and noise convolutional interference.
机译：拖曳雷达活跃干扰引起了在空对的风洞战斗的军事斗争中引起了很多关注，因为其结构简单，效率显着。雷达干扰模式变得越来越复杂。为了实现牵引诱饵的识别，有必要对干扰图案进行分类和识别。不同干扰模式的时频图像之间的差异是分类和识别的关键。深度学习为分类算法提供了具有强大的图像数据处理功能的分类算法。因此，在本文中，旨在牵引诱饵干扰，卷积神经网络擅长深度学习的图像分析，应用于雷达主动干扰图案时频图像分类和识别技术。仿真实验部门使用卷积神经网络（RENEXT残余网络）进行分类和验证密集假目标干扰和噪声卷积干扰的两种不同的干扰模式。

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