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通过时间波形频谱一致性对有限样本的特定发射体识别方法.
Chunyang Tang1,2, Jing Lian1, Li Zheng1
1School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究引入了使用TFC-CNN的特定发射者识别 (SEI) 的新方法,在有限的数据中提高了准确性. 该技术提高了无线电发射器的识别性能,即使训练样本稀少.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 无线电频率工程 无线电频率工程
背景情况:
- 特定发射器识别 (SEI) 使用无线电信号特征来识别发射器.
- 深度学习已经改善了SEI,但与短时间或低频发射器的有限训练数据作斗争.
- 当深度学习模型在稀缺样本上接受训练时,不足降低了准确性.
研究的目的:
- 为了应对SEI中有限的样本和数据稀缺性的发射器分类的挑战.
- 提出一种新的TFC-CNN方法,以在数据有限的条件下提高SEI性能.
主要方法:
- 利用连续波段变换 (CWT) 来增强数据,创建时间波段频谱对.
- 使用复杂值神经网络 (CVNN) 和深卷积神经网络 (DCNN) 来进行特征提取.
- 训练模型使用正常化的温度尺度交叉 (NT-Xent) 和交叉 (CE) 损失与共弦值损失来实现特征一致性.
主要成果:
- 与WiFi和ADS-B数据集上现有的最先进的方法相比,TFC-CNN方法显示出更高的性能.
- 在ADS-B测试数据集上,只用5%的培训样本,实现了84.10%的识别准确度.
- 在仅5%的培训样本中,在WiFi测试数据集上实现了96.99%的识别准确度.
结论:
- 提议的TFC-CNN方法有效地处理SEI任务,只有少数样本,性能优于传统方法.
- 该技术在识别非法发送者和在数据有限的身份验证系统中显示出巨大的潜力.
- 通过CWT和先进的神经网络架构进行数据增强是实现低数据SEI场景中高精度的关键.
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