面向非均衡类别的半监督辐射源识别方法

谭凯文; 张立民; 闫文君; 徐从安; 凌青; 刘恒燕

doi:10.12000/JR22043

面向非均衡类别的半监督辐射源识别方法

DOI: 10.12000/JR22043

海军航空大学信息融合研究所烟台 264001

基金项目: 国家自然科学基金(91538201)

详细信息

作者简介:
谭凯文(1998-)，男，硕士研究生，主要研究方向为特定辐射源识别、生成式对抗网络、频谱感知

张立民(1966-)，男，教授，博士生导师，主要研究方向为卫星信号处理及应用

闫文君(1986-)，男，博士，副教授，主要研究方向为空时分组码检测、智能信号处理

徐从安(1987-)，男，博士，副教授，主要研究方向为遥感图像智能处理、多目标跟踪

凌　青(1987-)，女，博士，副教授，主要研究方向为通信信号智能处理

刘恒燕(1994-)，女，博士研究生，主要研究方向为LDPC译码

通讯作者:
张立民 iamzlm@163.com

闫文君 wj_yan@foxmail.com

责任主编：朱卫纲 Corresponding Editor: ZHU Weigang
中图分类号: TN911.7
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- 被引次数: 0
出版历程
- 收稿日期: 2022-03-11
- 修回日期: 2022-05-05
- 网络出版日期: 2022-05-17
- 刊出日期: 2022-08-28

A Semi-supervised Emitter Identification Method for Imbalanced Category

Institute of Information Fusion, Naval Aviation University, Yantai 264001, China

Funds: The National Natural Science Foundation of China (91538201)

More Information

Corresponding author: ZHANG Limin, iamzlm@163.com; YAN Wenjun, wj_yan@foxmail.com

摘要

摘要: 针对辐射源个体识别(SEI)中样本标签不完整和数据类别分布不平衡导致分类准确率下降的问题，该文提出了一种基于代价敏感学习和半监督生成式对抗网络(GAN)的特定辐射源分类方法。该方法通过半监督训练方式优化生成器和判别器的网络参数，并向残差网络中添加多尺度拓扑模块融合时域信号的多维分辨率特征，赋予生成样本额外标签从而直接利用判别器完成分类。同时设计代价敏感损失缓解优势样本导致的梯度传播失衡，改善分类器在类不平衡数据集上的识别性能。在4类失衡仿真数据集上的实验结果表明，存在40%无标记样本的情况下，该方法对于5个辐射源的平均识别率相比于交叉熵损失和焦点损失分别提高5.34%和2.69%，为解决数据标注缺失和类别分布不均条件下的特定辐射源识别问题提供了新思路。
- 代价敏感学习 /
- 生成式对抗网络 /
- 非均衡类别 /
- 特定辐射源识别 /
- 半监督学习
Abstract: This paper proposes an SEI method based on cost-sensitive learning and semisupervised generative adversarial networks to address the problem of incomplete sample labels and imbalanced data category distribution in Specific Emitter Identification (SEI), which leads to a decline in inaccuracy. Through semisupervised training, the method optimizes the network parameters of the generator and discriminator, adds a multiscale topological block to ResNet to fuse the multi-dimensional resolution features of the time-domain signal, and attributes additional labels to the generated samples to directly use the discriminator to complete the classification. Simultaneously, a cost-sensitive loss is designed to alleviate the imbalance of gradient propagation caused by the dominant samples and improve the recognition performance of the classifier on the class-imbalanced dataset. The experimental results on four types of imbalanced datasets show that in the presence of 40% unlabeled samples, the average recognition accuracy for five emitters is improved by 5.34% and 2.69%, respectively, compared with the cross-entropy loss and focus loss. This provides a new idea for solving the problem of SEI under the conditions of insufficient data labels and an unbalanced distribution of data.
- Cost-sensitive learning /
- Generative Adversarial Network (GAN) /
- Imbalanced category /
- Specific Emitter Identification (SEI) /
- Semi-supervised learning

HTML全文

图 1 IC-SGAN整体结构

Figure 1. The overall structure of IC-SGAN

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图 2 基于MTB和残差单元构建的判别网络

Figure 2. The discriminator based on MTB and residual units

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图 3 5类代价敏感损失曲线

Figure 3. Five types of cost-sensitive loss curve

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图 4 基本神经元结构

Figure 4. The basic neuron structure

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图 5 判别器和生成器损失值变化曲线

Figure 5. The loss value change of thediscriminator and generator

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图 6 判别器结构参数对识别准确率的影响

Figure 6. The influence of discriminator structure parameters on recognition accuracy

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图 7 MTB-ResNet与ResNet的识别准确率对比

Figure 7. Comparison of recognition accuracy between MTB-ResNet and ResNet

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图 8 不同训练样本数下识别性能对比

Figure 8. Comparison of recognition performance under different numbers of training samples

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图 9 不同标记比下的混淆矩阵

Figure 9. Confusion matrix under different labeled ratios

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图 10 辐射源信号采集系统实物图

Figure 10. Physical map of emitter signal acquisition system

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图 11 IC-SGAN对于不同辐射源数量的识别结果

Figure 11. The recognition results for different numbers of emitters

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表 1 生成器结构

Table 1. The structure of generator

层名称	参数设置
输入层随机噪声	1×100
全连接层维度变换	256×64 1×256×64
卷积层1，批归一化， LeakyReLU，上采样	Filters = 128, kernel_size = 1×3, strides = 1, padding = same
卷积层3	Filters = 128
卷积层4	Filters = 64
卷积层5	Filters = 32
卷积层6	Filters = 1
Flatten，全连接层输出层	1000 1×1000信号向量

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表 2 仿真数据的训练集和测试集设置

Table 2. Training and test set settings for simulation data

数据集	类别
数据集	E1	E2	E3	E4	E5
标准训练集	480	480	480	480	480
标准测试集	480	480	480	480	480
训练集1	10	10	100	480	480
训练集2	10	20	200	180	480
训练集3	480	480	480	20	80
训练集4	50	50	180	480	300

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表 3 不同损失函数在不同信噪比下的性能评估(%)

Table 3. Performance evaluation of loss functions under different SNRs (%)

训练集	损失函数	0 dB	2 dB	4 dB	6 dB	8 dB	10 dB	12 dB	14 dB	16 dB	18 dB	20 dB	22 dB	24 dB
标准训练集	CE	49.33	51.17	53.36	58.64	65.47	72.44	85.16	90.88	94.64	96.48	99.23	99.76	99.80
	FL	48.78	50.97	54.29	57.97	64.31	70.84	86.10	91.12	93.77	92.57	98.68	98.56	98.65
	ICL	48.31	51.26	54.33	56.19	62.99	68.09	85.84	89.55	94.05	95.69	96.02	98.27	99.31
训练集1	CE	29.88	35.14	37.32	39.24	39.88	40.04	41.52	42.63	45.68	48.52	74.72	79.87	82.61
	FL	30.13	33.16	36.08	38.84	40.40	42.32	41.96	43.08	45.12	48.60	74.16	80.77	84.16
	ICL	30.65	36.47	35.04	38.39	41.26	43.77	44.27	45.46	49.00	54.24	74.84	83.13	85.36
训练集2	CE	19.77	24.13	25.80	29.48	30.56	39.72	42.76	54.52	55.20	75.96	79.56	80.15	81.36
	FL	23.08	25.55	24.16	25.76	33.16	36.24	36.44	55.80	57.24	73.00	80.24	81.66	83.48
	ICL	24.11	26.67	26.77	28.94	35.29	40.94	42.96	53.97	59.33	77.04	79.64	81.08	84.70
训练集3	CE	28.09	32.40	33.57	36.74	42.17	46.38	49.44	50.36	55.56	64.36	73.49	77.76	79.40
	FL	33.68	33.19	38.45	38.77	42.54	45.29	48.37	51.94	52.79	66.81	74.31	77.56	78.04
	ICL	32.41	36.97	40.60	40.88	44.28	46.00	49.64	50.96	56.44	65.52	75.88	79.54	80.92
训练集4	CE	30.11	31.79	35.88	36.08	43.12	45.00	48.36	60.08	62.03	64.28	70.80	72.61	78.54
	FL	31.25	33.57	36.40	40.36	42.96	43.84	54.56	62.36	64.40	64.88	74.16	73.77	79.76
	ICL	32.97	33.22	36.42	41.32	44.04	47.52	56.24	64.76	67.04	68.24	81.60	82.77	83.84

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表 4 不同算法识别准确率对比(%)

Table 4. Comparison of recognition accuracy of different schemes (%)

算法	0 dB	4 dB	8 dB	12 dB	16 dB	20 dB	24 dB
本文方法+CE	30.11	35.88	43.12	48.36	62.03	70.80	78.54
本文方法+ICL	32.97	36.42	44.04	56.24	67.04	81.60	83.84
方法1+CE	27.53	31.76	37.72	44.80	59.36	64.43	73.36
方法1+ICL	28.12	32.64	39.53	49.08	63.96	72.12	75.24
方法2+CE	26.46	33.18	41.07	46.08	60.47	68.00	77.36
方法2+ICL	29.16	34.56	42.28	51.32	65.23	75.16	79.00
方法3	34.82	33.46	40.61	50.92	57.77	67.18	71.49
方法4	20.96	25.10	34.64	45.69	57.14	58.06	67.18

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表 5 网络复杂度对比

Table 5. Network complexity comparison

网络模型	空间复杂度 N_O (M)	迭代平均耗时t_train (s)	平均识别时间t_test (s)
IC-SGAN	1.97+3.93	90.8	3.1
RFFE-InfoGAN	2.52+19.57	207.0	4.6
E3SGAN	4.25+4.61	110.5	2.8

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表 6 真实数据的训练集和测试集设置

Table 6. Training set and test set settings for real data

数据集	类别
数据集	E1	E2	E3	E4	E5	E6	E7	E8	E9	E10
标准训练集	480	480	480	480	480	480	480	480	480	480
标准测试集	240	240	240	240	240	240	240	240	240	240
训练集1	48	480	48	480	48	480	48	480	48	480
训练集2	120	480	120	480	120	480	120	480	120	480
训练集3	480	48	480	48	480	48	480	48	480	48
训练集4	480	120	480	120	480	120	480	120	480	120

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表 7 3类损失函数的识别性能评估(%)

Table 7. Recognition performance evaluation of three loss functions (%)

训练集	损失函数	K = 5	K = 6	K = 7	K = 8	K = 9	K = 10	Average
标准训练集	CE	93.52	89.78	87.27	85.34	85.47	86.80	88.03
	FL	97.39	87.64	88.21	89.66	87.28	85.32	89.25
	ICL	94.60	88.36	90.46	88.56	86.72	86.38	89.18
训练集1	CE	90.16	78.35	76.40	72.38	70.87	62.76	75.15
	FL	91.68	79.76	79.35	74.61	74.98	65.18	77.59
	ICL	93.48	84.07	82.37	77.56	72.65	68.92	79.84
训练集2	CE	86.88	80.17	78.46	70.02	69.42	71.43	76.06
	FL	91.54	78.12	81.98	68.33	72.13	72.16	77.37
	ICL	90.53	83.41	76.29	72.21	74.87	68.59	77.65
训练集3	CE	88.57	79.44	74.67	71.62	73.51	66.94	75.79
	FL	91.05	82.56	76.26	69.50	76.38	69.48	77.53
	ICL	93.37	84.50	79.18	75.43	78.86	72.86	80.70
训练集4	CE	86.72	79.98	83.43	74.34	73.29	65.74	77.25
	FL	84.07	82.40	77.53	77.07	75.82	68.09	77.49
	ICL	83.46	84.98	79.25	77.33	79.48	71.38	79.31

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