在SEI模型中评估混行为
Brennan Olds1, Ethan Maas1, Alan J Michaels1
1Virginia Tech National Security Institute, Blacksburg, VA 24060, USA.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
用于特定发射器识别的无线电频率机器学习模型通常通过在低信号对噪声比率下选择少数类而失败. 合奏模型更强大,但并不总是表现最好.
科学领域:
- 无线电频率机器学习 (RFML)
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 特定发射器识别 (SEI) 使用RFML进行信号分类.
- 现有的研究重点是SEI的成功模型架构.
- 在SEI的RFML中,系统性失败和学习的行为被低估了.
研究的目的:
- 调查基于RFML的SEI模型中的故障模式.
- 在各种模型架构中分析分类错误.
- 检查信号噪声比 (SNR) 和训练数据量对SEI性能的影响.
主要方法:
- 在64无线电SEI数据集上评估了多个RFML模型架构.
- 对信号噪声比 (SNR) 和训练数据量进行控制.
- 错误分类结果中孤立的常见模式.
主要成果:
- 在RFML模型中,常常会默认使用一小部分类 (约. 10%),随着SNR的下降.
- 在不同的SEI模型和架构中,错误模式是一致的.
- 组合模型在低SNR时表现出更大的弹性,但在高SNR时并不理想.
结论:
- 了解RFML故障模式对于强大的SEI系统至关重要.
- 模型的脆弱性随着SNR的下降而增加.
- 组合方法在SEI中提供了稳定性和峰值性能之间的权衡.
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