基于集成深度学习的有源干扰智能分类  

Ensemble deep learning-based intelligent classification of active jamming

作　　者：吕勤哲 全英汇 沙明辉 董淑仙 邢孟道[3] LYU Qinzhe;QUAN Yinghui;SHA Minghui;DONG Shuxian;XING Mengdao(School of Electronic Engineering,Xidian University,Xi’an 710071,China;Beijing Institute of Radio Measurement,Beijing 100854,China;Academy of Advanced Interdisciplinary Research,Xidian University,Xi’an 710071,China)

机构地区：[1]西安电子科技大学电子工程学院,陕西西安710071 [2]北京无线电测量研究所,北京100854 [3]西安电子科技大学前沿交叉研究院,陕西西安710071

出　　处：《系统工程与电子技术》2022年第12期3595-3602,共8页Systems Engineering and Electronics

基　　金：国家自然科学基金(61772397);国家重点研发计划(2016YFE0200400);陕西省科技创新团队(2019TD-002)资助课题。

摘　　要：针对现有基于机器学习的雷达有源干扰分类大多需要构建人工特征集且小样本情况下分类精度低的问题,提出一种基于多通道特征融合的集成卷积神经网络(convolutional neural network,CNN)分类方法。首先,建立多种有源干扰的数学模型,仿真并利用短时傅里叶变换获得其时频分布图;其次,提取时频分布图的实部、虚部和模值三通道特征,通过多种特征组合方式建立不同特征组合的样本集;最终,构建以CNN为基分类器的集成深度学习模型,每个CNN分别提取不同样本集的特征,对所有基分类器的预测结果做多数投票得到集成模型的整体预测结果。实验表明,该方法能够有效实现小样本情况下多类有源干扰的高精度智能化识别。Aiming at the problems that most existing machine learning-based classification of radar active jamming need to construct artificial feature sets and low classification accuracy in the case of small samples,an ensemble convolutional neural network(CNN)classification method based on multichannel feature fusion is proposed.Firstly,multiple mathematical models of active jamming are established,simulated and the corresponding time-frequency profiles are obtained by using the short-time Fourier transform(STFT).Secondly,real part,imaginary part,and modulus three-channel features of time-frequency distribution plots are extracted to establish sample sets containing different combinations of features through multiple feature combinations.Ultimately,an ensemble depth model with CNN as the base classifier is constructed,each CNN separately extracts the features of different sample sets,and majority voting on the prediction results of all base classifiers gives the overall prediction results of the ensemble model.The experiments show that the proposed method can effectively realize highly accurate intelligent identification of multiple classes of active jamming in the case of small samples.

关 键 词：有源干扰分类 短时傅里叶变换 集成学习 卷积神经网络 小样本 

分 类 号：TN974[电子电信—信号与信息处理]