基于轻量化卷积神经网络的雷达干扰识别技术研究OA北大核心CSTPCD
Radar Interference Recognition Based on Lightweight Convolutional Neural Network
随着科学技术的不断革新,当前电子战形势日益复杂,雷达面临的电子干扰呈高相参、强欺骗、隐匿性、低功率等特性,严重削弱了雷达的探测和跟踪性能,甚至使其失去作战能力.因此,精准识别雷达面临的有源干扰样式是雷达系统进行针对性干扰抑制的前提.轻量化卷积神经网络(MobileNet)无需人为提取特征便能有效捕获图像中的空间结构信息,在图像处理及分类领域表现优异.文中提出了基于MobileNet的雷达干扰识别模型,应用对雷达有源干扰的时频特性数据集验证模…查看全部>>
With the advancement of technology,the current electronic warfare landscape has become increasingly complex.Radar systems are confronted with electronic interference characterized by high coherence,strong deception,stealthiness and low power.This significantly degrades their detection and tracking capabilities,potentially rendering them combat ineffective.Therefore,the accurate identification of the types of active interference faced by radars is crucial for…查看全部>>
张海舟;贺青;马泽强;黄亮;李宗阳
南京电子技术研究所,江苏 南京 210039南京电子技术研究所,江苏 南京 210039南京电子技术研究所,江苏 南京 210039南京电子技术研究所,江苏 南京 210039南京电子技术研究所,江苏 南京 210039
电子信息工程
雷达有源干扰轻量化卷积神经网络分类
radaractive interferencelightweight convolutional neural network(MobileNet)classification
《现代雷达》 2024 (6)
79-84,6
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