基于深度神经网络的空中目标与箔条识别算法  被引量:1

Recognition Algorithm of Aerial Targets and Chaff Clouds Based on Deep Neural Network

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作  者:柴峥豪 王凤杰 刘亚奇 薛广涛[1] CHAI Zhenghao;WANG Fengjie;LIU Yaqi;XUE Guangtao(School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;Shanghai Radio Equipment Research Institute,Shanghai 201109,China)

机构地区:[1]上海交通大学电子信息与电气工程学院,上海200240 [2]上海无线电设备研究所,上海201109

出  处:《制导与引信》2023年第4期42-47,共6页Guidance & Fuze

摘  要:针对现有雷达箔条干扰识别算法存在的泛化能力不强、依赖人工特征提取和先验知识等问题,提出了基于深度神经网络的空中目标与箔条的识别算法。采用以时间卷积网络为主干的深度神经网络设计模型,对空中目标和箔条雷达回波的不同特征进行学习,从而对空中目标进行识别。采用实测雷达回波数据进行验证,结果表明,基于时间神经卷积网络的空中目标与箔条识别算法识别性能较好。该算法为雷达抗箔条干扰的研究提供了新的思路。Aiming at the problems of the low generalization ability,the reliance on the artificial feature extraction and prior knowledge in the recognition algorithms of anti-chaff interference for radar equipment,a recognition algorithm of aerial targets and chaff clouds based on deep neural network was put forward.The deep neural network with temporal convolutional network(TCN)as backbone was utilized to design the model,to learn the different features between aerial targets and chaff clouds in radar echo and recognize the aerial targets.The verification results on the measured radar echo data indicate that the recognition algorithm performance of aerial targets and chaff clouds based on deep neural network is relatively significant,which provides a new idea for the research on radar antichaff interference.

关 键 词:箔条干扰 目标识别 深度神经网络 时间卷积网络 

分 类 号:TN974[电子电信—信号与信息处理]

 

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