基于重要区域定位与掩码的射频指纹可视化分析

刘文斌; 范平志; 杨佳煌; 李雨锴; 王钰浩; 孟华

doi:10.11884/HPLPB202436.230380

基于重要区域定位与掩码的射频指纹可视化分析

doi: 10.11884/HPLPB202436.230380

刘文斌^{1, 2,},
范平志¹,
杨佳煌³,
李雨锴³,
王钰浩³,
孟华^3, ,

1.
西南交通大学信息科学与技术学院，成都 611756
2.
中国电子科技集团公司第三十研究所，成都 610041
3.
西南交通大学数学学院，成都 611756

基金项目: 西南交通大学交叉培育项目（2682023TPY027）

详细信息

作者简介:
刘文斌，bingege389@sina.com.cn

通讯作者:
孟　华，menghua@swjtu.edu.cn。

中图分类号: TN92
计量
- 文章访问数: 83
- HTML全文浏览量: 41
- PDF下载量: 23
- 被引次数: 0
出版历程
- 收稿日期: 2023-10-30
- 修回日期: 2023-12-19
- 录用日期: 2024-01-22
- 网络出版日期: 2024-03-15
- 刊出日期: 2024-02-29

Visual analysis method for RF fingerprint based on important region localization and masking

Liu Wenbin^{1, 2
,},
Fan Pingzhi¹,
Yang Jiahuang³,
Li Yukai³,
Wang Yuhao³,
Meng Hua^{3
, ,}

1.
School of Information Science & Technology, Southwest Jiaotong University, Chengdu 611756, China
2.
The 30th Research Institute of China Electronics Technology Group Corporation, Chengdu 610041, China
3.
School of Mathematics, Southwest Jiaotong University, Chengdu 611756, China

摘要

摘要: 针对时域脉冲信号样本的射频指纹提取与深度学习模型的可解释性，提出了一种基于Grad-CAM的重要区域可视化呈现方法，并通过重要区域的多次掩码测试，来分析重要区域对射频指纹识别结果的影响。基于10个辐射源的信号样本，对比了层数不同的两种ResNet模型的测试结果。测试发现该方法能够区分不同类型信号并呈现个体差异。分析表明，该方法能够发现不同辐射源发送相同信号时的重要区域定位差异，能可视化反映辐射源个体特征的空间距离，以及不同模型的特征表征与指纹定位准确度差异；同时发现对重要区域的掩码更容易产生误预测，证明特定信号存在与时频特征相关的射频指纹，并可辅助可视化定位影响射频指纹样本识别的关键点。
- 可解释性 /
- 射频指纹 /
- 深度学习 /
- 可视化 /
- 信号特征
Abstract: A Grad-CAM based visualizing method for important regions is proposed for the interpretability of RF fingerprint extraction and deep learning models of time-domain pulse signal samples. The impact of important regions on RF fingerprint recognition results is analyzed through multiple mask tests of important regions. Based on signal samples of 10 emitters, the test results of two ResNet models with different layers are compared. It is found that the proposed method can distinguish different types of signals and present individual differences. Analysis shows that this method can detect important regional localization differences when different emitters send the same signal, and can visually reflect the spatial distance of RF fingerprint characteristics, as well as the differences in feature representation and fingerprint localization accuracy of different models; At the same time, it is found that masks for important areas are more prone to false predictions, which proves the existence of RF fingerprints related to time-frequency characteristics in specific signals, and can assist in visualizing key points that affect the recognition of RF fingerprint samples.
- interpretability /
- radio frequency fingerprint /
- deep learning /
- visualization /
- signal characteristics

HTML全文

图 1 LittleResNet与MiddleResNet网络结构

Figure 1. Network architecture of MiddleResNet and LittleResNet

下载: 全尺寸图片幻灯片

图 2 两种模型的时域脉冲信号热力图对比

Figure 2. Comparison of time-domain pulse signal heat maps between MiddleResNet and LittleResNet

下载: 全尺寸图片幻灯片

图 3 MiddleResNet模型和LittleResNet模型的各对象的重要区域呈现

Figure 3. Important areas of emitters when using MiddleResNe and LittleResNet

下载: 全尺寸图片幻灯片

图 4 样本掩码测试对比

Figure 4. Test results by continuous mask and different samples comparison

下载: 全尺寸图片幻灯片

表 1 三种模型的性能对比结果

Table 1. Performance comparison results of three models

model	accuracy				parameter number/10⁶
model	5 dB	9 dB	14 dB	all data	parameter number/10⁶
LittleResNet	0.7605	0.8775	0.9605	0.8812	0.153
MiddleResNet	0.7665	0.8990	0.9685	0.8930	0.254
Transformer	0.8670	0.9493	0.995	0.9439	1.021

下载: 导出CSV

表 2 预测错误的样本数量及其序号

Table 2. Number and sequence of mispredicted samples

emitter	Transformer	MiddleResNet	LittleResNet
1#	1 (sample23)	4 (sample3,5,12,17)	4 (sample5,10, 12, 23)
2#	0	7 (sample3,5,7,14,19,22,28)	6 (sample3,6,7,19,22,28)
3#	0	0	0
4#	1 (sample19)	2 (sample7,19)	2 (sample7,19)
5#	1 (sample9)	1 (sample14)	0
6#	0	0	0
7#	0	0	0
8#	0	0	0
9#	0	0	0
10#	0	0	0

下载: 导出CSV

表 3 MiddleResNet模型3#辐射源对象掩码预测结果

Table 3. Prediction results when masking 3# and using the MiddleResNet model

sample	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30
no masking	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3
1^st masking	3	3	2	3	3	3	3	3	1	3	3	1	3	3	3	3	3	3	3	3	3	3	3	3	2	3	2	3	3	3
2^nd masking	3	3	2	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	2	3	3	3
3^rd masking	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	2	3	3	3	2	3	3	3

下载: 导出CSV

表 4 无掩码到3次掩码时误预测样本数量与误预测成的对象

Table 4. Number of mispredicted samples and emitter(s) mainly mispredicted to when masking different times

emitter	MiddleResNet					LittleResNet
emitter	no masking	1^st masking	2^nd masking	3^rd masking	emitter(s) mainly mispredicted to	no masking	1^st masking	2^nd masking	3^rd masking	emitter(s) mainly mispredicted to
1#	4	27	29	29	2#	4	10	10	10	2#
2#	7	16	19	19	1#、3#	6	10	14	10	1#、3#
3#	0	5	2	2	2#、1#	0	0	0	0	——
4#	2	13	27	29	3#	2	12	16	10	3#、2#、5#、1#
5#	1	20	29	29	3#、4#	0	5	14	17	3#
6#	0	13	28	30	7#、10#	0	15	23	25	7#、10#
7#	0	7	9	16	10#、3#	0	1	1	7	10#、3#
8#	0	9	20	28	10#、7#、9#、3#	0	7	13	16	10#、7#、6#、9#
9#	0	8	14	17	10#、7#、3#	0	6	13	16	10#
10#	0	2	11	15	7#、3#	0	2	3	4	7#

下载: 导出CSV

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