基于CNN-BiLSTM-AM的雷达波形设计  

Radar Waveform Design Based on CNN-BiLSTM-AM

作　　者：董军 杜晓林 何肖阳 李建波 田团伟 DONG Jun;DU Xiaolin;HE Xiaoyang;LI Jianbo;TIAN Tuanwei(School of Computer and Control Engineering,Yantai University,Yantai Shandong 264005,China;School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;School of Physics and Electronics,Henan University,Kaifeng Henan 475001,China)

机构地区：[1]烟台大学计算机与控制工程学院,山东烟台264005 [2]重庆邮电大学通信与信息工程学院,重庆400065 [3]河南大学物理与电子学院,河南开封475001

出　　处：《现代雷达》2025年第3期72-79,共8页Modern Radar

基　　金：国家自然科学基金资助项目(61801415);重庆市教委科学技术研究资助项目(KJQN202100605)。

摘　　要：在实战环境中,复杂的电磁环境会导致雷达无法获得满足性能所需要的先验信息,且雷达已不再局限于单一任务和工作模式。为实现电子战中雷达的多方面性能提升,文中提出了一种基于注意力机制(AM)与卷积神经网络-双向长短时记忆(CNN-BiLSTM)的雷达波形设计方法。首先,利用环境信息,基于互信息(MI)准则和信干噪比(SINR)准则构建数据集;然后,搭建了CNN-BiLSTM-AM神经网络模型,再利用数据集进行训练和测试对比,从而实现利用网络来生成波形的目的;最后,通过仿真实验验证了所提方法能够有效兼顾多准则性能,平均雷达综合性能较MI准则提升1.14%,较SINR准则提升2.97%。In the actual combat environment,complex electromagnetic environments can result in radars that do not have access to the prior information needed to meet performance,and radars are no longer limited to a single mission and operating mode.To achieve multiple performance improvements of radar in electronic warfare,a waveform design method based on attention mechanism(AM)and convolutional neural network-bidirectional long short term memory(CNN-BiLSTM)is proposed in this paper.Firstly,the environmental information is used to build a data set via mutual information(MI)criterion and signal-to-interference-plus-noise ratio(SINR)criterion.Secondly,a CNN-BiLSTM-AM neural network model is established,and the data set is utilized for training and test comparison,so as to achieve the purpose of using the network to generate waveforms.Finally,simulation experiments verify that the proposed method can effectively balance multi-criteria performance,with an average radar comprehensive performance improvement of 1.14%compared to the MI criterion and 2.97%compared to the SINR criterion.

关 键 词：波形优化设计 神经网络 注意力机制 信干噪比准则 互信息准则 

分 类 号：TN97[电子电信—信号与信息处理]