杂波条件下利用一维卷积神经网络的认知雷达波形设计

赵俊龙; 李伟; 甘奕夫; 邹鲲

doi:10.7652/xjtuxb202104008

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杂波条件下利用一维卷积神经网络的认知雷达波形设计

更新时间：2025-07-09

- 杂波条件下利用一维卷积神经网络的认知雷达波形设计
- A Design Method of Cognitive Radar Waveform Using One-Dimensional Convolutional Neural Network in the Presence of Clutter
- 西安交通大学学报 2021年55卷第4期页码：69-76
- 作者机构：
  
  西安空军工程大学信息与导航学院,西安,710077
- 作者简介：
- 基金信息：
  
  : 2020-08-04。作者简介: 赵俊龙(1995—),男,硕士生;李伟(通信作者),男,副教授,博士生导师。基金项目: 国家自然科学基金资助项目(61571456);航空科学基金资助项目(20160196001);陕西省自然科学基金资助项目(2020JM-347)
- DOI：10.7652/xjtuxb202104008
  中图分类号： TN951
- 网络首发：2021-04-10，
  
  纸质出版：2021
- 稿件说明：
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赵俊龙, 李伟, 甘奕夫, 等. 杂波条件下利用一维卷积神经网络的认知雷达波形设计[J]. 西安交通大学学报, 2021,55(4):69-76.

A Design Method of Cognitive Radar Waveform Using One-Dimensional Convolutional Neural Network in the Presence of Clutter[J]. 2021, 55(4): 69-76.
赵俊龙, 李伟, 甘奕夫, 等. 杂波条件下利用一维卷积神经网络的认知雷达波形设计[J]. 西安交通大学学报, 2021,55(4):69-76. DOI： 10.7652/xjtuxb202104008.

A Design Method of Cognitive Radar Waveform Using One-Dimensional Convolutional Neural Network in the Presence of Clutter[J]. 2021, 55(4): 69-76. DOI： 10.7652/xjtuxb202104008.

摘要

针对单准则设计的波形难以满足雷达多状态和多任务的问题

提出了一种杂波条件下利用一维卷积神经网络的认知雷达波形设计(CRWD-1D-CNN)方法。首先

设定环境变量

并根据互信息准则和信干噪比准则来构建训练集和测试集; 其次

根据数据集的一维数据形式和采样点数

设计一个包含3个卷积层、2个全连接层的1D-CNN模型; 最后

使用训练集对1D-CNN模型进行训练

利用1D-CNN对一维数据之间非线性关系的学习能力来学习互信息准则和信干噪比准则

然后

使用训练后的1D-CNN生成波形。为衡量雷达综合性能

提出了一种目标最终识别率指标。实验结果表明

采用CRWD-1D-CNN方法设计的波形作为雷达发射信号时

与使用互信息准则生成的波形相比

雷达综合性能平均提升0.64%

与使用信干噪比准则生成的波形相比平均提升2.13%

证明了CRWD-1D-CNN方法可联合互信息准则和信干噪比准则

提高雷达综合性能。

Abstract

A design method of cognitive radar waveform(CRWD-1D-CNN)using one-dimensional convolutional neural network(1D-CNN)in the presence of clutter is proposed to solve the problem that the waveform designed using single-criterion is difficult to satisfy the radar multi-state and multi-task. Firstly

environment variables are set

and a training set and a test set are constructed according to the mutual information(MI)criterion and the signal-to-interference and noise ratio criterion(SINR); Secondly

a 1D-CNN model containing 3 convolutional layers and 2 full connection layers is designed based on the data form and the number of sampling points; Finally

the 1D-CNN model is trained with the training set

and the learning ability of the 1D-CNN is made use of to learn the MI criterion and SINR criteria

then the trained 1D-CNN is used to generate waveform. An index of target final recognition rate and an index of target recognition rate index are proposed to measure the comprehensive performance of radar. Simulation results show that when the waveform designed by CRWD-1D-CNN method is used as radar transmitting signal

the overall performance of the radar is improved by 0.64% and 2.13% on average compared with using the waveforms generated by using MI and SINR criteria. These results prove that the CRWD-1D-CNN method can effectively combine the MI criterion and SINR criterion

and improve the comprehensive performance of radar.

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