基于元学习的畸变雷达电磁信号识别

颜康; 金炜东; 黄颖坤; 葛鹏; 朱劼昊

doi:10.11999/JEIT210190

基于元学习的畸变雷达电磁信号识别

doi: 10.11999/JEIT210190

1.
西南交通大学电气工程学院成都 610097
2.
电子信息控制重点实验室成都 610036

基金项目: 电子信息控制重点实验室项目开放基金(6142105190312)

详细信息

作者简介:
颜康：男，1988年生，博士生，研究方向为雷达信号处理、机器学习

金炜东：男，1959年生，教授，博士生导师，研究方向为智能信息处理、系统仿真与优化方法

黄颖坤：男，1989年生，博士生，研究方向为雷达信号处理、机器学习

葛鹏：男，1986年生，讲师，研究方向为雷达信号处理、电子对抗

朱劼昊：男，1983年生，高级工程师，研究方向为雷达自动目标识别和目标特性分析

通讯作者:
金炜东　wdjin@home.swjtu.edu.cn

中图分类号: TN95
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出版历程
- 收稿日期: 2021-03-05
- 修回日期: 2021-11-03
- 录用日期: 2021-11-03
- 网络出版日期: 2021-12-19
- 刊出日期: 2022-04-18

Distorted Radar Electromagnetic Signal Recognition Based on Meta-learning

1.
College of Electrical Engineering , Southwest Jiaotong University, Chengdu 610097, China
2.
The Science and Technology on Electronic Information Control Laboratory, Chengdu 610036, China

Funds: The Science and Technology on Electronic Information Control Laboratory (6142105190312)

摘要

摘要: 畸变雷达电磁信号会严重影响雷达侦察装备的探测性能。如何有效地识别畸变信号类型对侦察系统的精确感知具有重要现实意义。针对畸变雷达信号往往存在样本稀缺的问题，该文提出一种基于模型无关元学习的残差网络(MAML-ResNet)。算法首先利用正常雷达信号样本训练元学习器，然后在畸变信号样本进行精调，最后在仅有少量畸变信号样本下，实现畸变信号的识别。实验结果表明该算法有效地提高了在小样本数据下畸变信号的识别准确率。
- 畸变雷达信号识别 /
- 深度学习 /
- 小样本学习 /
- 元学习
Abstract: Distorted radar electromagnetic signals will seriously affect the detection performance of radar reconnaissance equipment. How to identify effectively the type of distorted signal has important practical significance for the accurate perception of radar systems. For distorted radar signals, there is often a problem of sample scarcity. A Residual Network based on Model-Agnostic Meta-Learning (MAML-ResNet) is proposed. The algorithm first uses normal radar signal samples to train the meta-learner, then the meta-learner is fine-tuned in the distorted signal samples. Finally, the distorted signal is recognized with only a small number of distorted signal samples. Experimental results show that the recognition accuracy of distorted signals under small sample data is effectively improved.
- Distorted radar signal recognition /
- Deep learning /
- Few-shot learning /
- Meta learning

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图 1 基于模型无关元学习的残差网络结构图

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图 2 5种畸变雷达信号时频图像

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图 3 残差模块

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表 1 算法MAML流程

输入：创建若干个任务$ p(\mathcal{T}) $，步长超参数$ \alpha $,$ \beta $
输出：模型参数${\boldsymbol{\theta}}$
(1) 随机初始化${\boldsymbol{ \theta}}$
(2) 循环1：
(3) 　　随机对若干个任务采样$ {\mathcal{T}_i} \sim p(\mathcal{T}) $
(4) 　　循环2（对所有的任务$ {\mathcal{T}_i} $）：
(5) 　　　　根据$ K $个样本计算梯度${\nabla _{\boldsymbol{\theta}} }{\mathcal{L}_{ {\mathcal{T}_i} } }\left( { {f_{\boldsymbol{\theta}} } } \right)$
(6) 　　　　利用梯度下降法更新参数：${ {\boldsymbol{\theta} } '_i}{\text{ = } }{\boldsymbol{\theta}} - \alpha {\nabla _{\boldsymbol{\theta}} }{\mathcal{L}_{ {\mathcal{T}_i} } }\left( { {f_{\boldsymbol{\theta}} } } \right)$
(7) 　　循环2结束
(8) 　　更新参数$\theta \leftarrow {\boldsymbol{\theta} } - \beta {\nabla _{\boldsymbol{\theta} } }\sum {_{ {\mathcal{T}_i} \sim p(\mathcal{T})} } {\mathcal{L}_{ {\mathcal{T}_i} } }\left( { {f_{ { { {\boldsymbol{\theta } }'}_i} } } } \right)$
(9) 循环1结束

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表 2 ResNet结构参数

层数	输出尺寸/像素	对应卷积层
Conv1	$ {\text{112}} \times {\text{112}} $
Conv2_x	$ {\text{56}} \times {\text{56}} $	$ \left[\begin{array}{cc}\text{3}\times \text{3}，& \text{64}\\ \text{3}\times \text{3}，& \text{64}\end{array}\right]\times \text{2} $
Conv3_x	$ {\text{28}} \times {\text{28}} $	$ \left[\begin{array}{cc}\text{3}\times \text{3}，& \text{128}\\ \text{3}\times \text{3}，& \text{128}\end{array}\right]\times \text{2} $
Conv4_x	$ {\text{14}} \times {\text{14}} $	$ \left[\begin{array}{cc}\text{3}\times \text{3}，& \text{256}\\ \text{3}\times \text{3}，& \text{256}\end{array}\right]\times \text{2} $
Conv5_x	$ {\text{7}} \times {\text{7}} $	$ \left[\begin{array}{cc}\text{3}\times \text{3}，& \text{512}\\ \text{3}\times \text{3}，& \text{512}\end{array}\right]\times \text{2} $
全连接层	$ {\text{1}} \times {\text{1}} $

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表 3 人工特征提取方法特征集合

分析域	人工特征	参考文献	特征维数	描述
时域	复杂度特征，包括多尺度信息熵、信息维数、盒维数和稀疏度	[7]	每种特征1维，总共4维	雷达辐射源信号的变化趋势
频率域	复杂度特征	[7]	每种特征1维，总共4维	雷达辐射源信号的变化趋势
频率域	多尺度信息熵特征	[10]	10维	频率域序列的时间变化趋势
时频域	小波脊频级联特征	[3]	7维	时-频曲线上的7个不同统计特征
其他	模糊函数特征	[8]	3维	表征信号模糊函数主脊能量分布走向、分布重心及其惯性半径的3个特征参数

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表 4 畸变雷达信号识别结果(%)

方法	信噪比0～6 dB		信噪比7～13 dB		信噪比14～20 dB
方法	5 way 5 shot	5 way 10 shot	5 way 5 shot	5 way 10 shot	5 way 5 shot	5 way 10 shot
Decision Tree	60.9±1.5	70.1±0.2	68.0±0.3	78.6±0.3	69.3±0.7	79.5±0.2
K-NN	63.2±0.3	74.6±0.2	77.8±0.2	84.2±0.2	79.0±0.2	85.3±0.1
SVM	58.1±0.7	73.3±0.2	73.6±0.4	85.0±0.1	73.8±0.7	86.8±0.1
ResNet	38.8±1.5	51.1±1.9	59.8±0.4	67.8±0.3	52.3±0.5	56.6±0.3
MAML-ResNet	76.8±0.2	82.3±0.1	86.6±0.1	87.1±0.1	89.9±0.1	94.3±0.1

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表 5 高信噪比元学习器下畸变雷达信号识别结果(%)

方法	信噪比0～6 dB		信噪比7～13 dB
方法	5 way 5 shot	5 way 10 shot	5 way 5 shot	5 way 10 shot
MAML-ResNet	72.7±0.2	78.9±0.1	83.7±0.1	86.3±0.1

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表 6 低信噪比元学习器下畸变雷达信号识别结果(%)

方法	信噪比7～13 dB		信噪比14～20 dB
方法	5 way 5 shot	5 way 10 shot	5 way 5 shot	5 way 10 shot
MAML-ResNet	84.6±0.1	88.3±0.1	90.9±0.1	94.4±0.1

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