面向雷达行为识别的多尺度卷积注意力网络

熊敬伟; 潘继飞; 毕大平; 杜明洋

doi:10.19665/j.issn1001-2400.20231005

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面向雷达行为识别的多尺度卷积注意力网络

电磁空间安全专栏 | 更新时间：2024-01-22

- 面向雷达行为识别的多尺度卷积注意力网络
- 暂无标题
- 西安电子科技大学学报 2023年50卷第6期页码：62-74
- 作者机构：
  
  国防科技大学电子对抗学院,安徽合肥 230000
- 作者简介：
  
  [ "熊敬伟(1995—),男,国防科技大学硕士研究生,E-mail:[email protected];" ]
  潘继飞(1978—),男,教授,E-mail:[email protected]
  [ "毕大平(1965—),男,教授,E-mail:[email protected];" ]
  [ "杜明洋(1994—),男,讲师,E-mail:[email protected]。" ]
- 基金信息：
  
  国家自然科学基金(62071476)
- DOI：10.19665/j.issn1001-2400.20231005
  中图分类号： TN971
- 收稿日期：2023-03-07，
  
  纸质出版日期：2023-12-20
- 稿件说明：
移动端阅览
熊敬伟, 潘继飞, 毕大平, 等. 面向雷达行为识别的多尺度卷积注意力网络[J]. 西安电子科技大学学报, 2023,50(6):62-74.
熊敬伟, 潘继飞, 毕大平, 等. 面向雷达行为识别的多尺度卷积注意力网络[J]. 西安电子科技大学学报, 2023,50(6):62-74. DOI： 10.19665/j.issn1001-2400.20231005.

DOI：

摘要

针对低信噪比条件下雷达信号特征提取难、识别稳定性低的问题

提出了一种基于深度分组卷积、多尺度卷积和自注意力机制的雷达行为模式识别算法

在不增加训练难度的情况下提高复杂环境下的识别能力。该算法先采用深度分组卷积在浅层网络分离弱相关的通道

再通过多尺度卷积代替常规卷积实现多维特征的提取

最后利用自注意力机制调整优化不同特征图的权值

抑制低相关和负相关的通道与空间带来的影响。对比实验表明

所提MSCANet在0~50%丢失脉冲和虚假脉冲条件下平均识别率达到约92.25%

与基线网络AlexNet、ConvNet、ResNet、VGGNet相比

准确率提升了约5%~20%

不同雷达行为模式识别稳定

模型具有更好的泛化性和鲁棒性。同时

消融实验证明了深度分组卷积、多尺度卷积和自注意力机制对模式识别的有效性。

Abstract

A radar behavior mode recognition framework is proposed aiming at the problems of difficult feature extraction and low recognition stability of the radar signal under a low signal-to-noise ratio

which is based on depth-wise convolution

multi-scale convolution and the self-attention mechanism.It improves the recognition ability in complex environment without increasing the difficulty of training.This algorithm employs depth-wise convolution to segregate weakly correlated channels in the shallow network.Subsequently

it utilizes multi-scale convolution to replace conventional convolution for multi-dimensional feature extraction.Finally

it employs a self-attention mechanism to adjust and optimize the weights of different feature maps

thus suppressing the influence of low and negative correlations in both channels and the spatial domains.Comparative experiments demonstrate that the proposed MSCANet achieves an average recognition rate of 92.25% under conditions of 0~50% missing pulses and false pulses.Compared to baseline networks such as AlexNet

ConvNet

ResNet

and VGGNet

the accuracy has been improved by 5% to 20%.The model exhibits stable recognition of various radar patterns and demonstrates enhanced generalization and robustness.Simultaneously

ablation experiments confirm the effectiveness of deep grouped convolution

multi-scale convolution

and the self-attention mechanism for radar behavior recognition.

关键词

Keywords

references

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