无线电信号识别使用双阶段时空网络与双光谱分析
Hongmei Bai1, Siming Li2, Yong Jia1
1College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究引入了一种使用射频 (RF) 信号识别无人机 (UAV) 的新方法. 双光谱分析和双阶段网络显著提高了无人机识别准确度.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 越来越多地使用无人驾驶飞行器 (UAV),需要强大的识别方法.
- 通过无线电频率 (RF) 信号可靠识别无人机对于安全和民用应用至关重要.
研究的目的:
- 开发基于射频信号的UAVs的时空特征提取和分类的先进框架.
- 提高无人机识别系统的准确性和可靠性.
主要方法:
- 利用双光谱估计来将1D射频信号转换为2D双光谱特征图,捕捉更高阶的光谱特征和非线性依赖.
- 实现了两阶段的神经网络:ResNet18用于从双光谱图中提取空间特征,LSTM用于学习时间依赖.
- 将框架应用于无人机射频信号的公共数据集,以分类在五个类别中.
主要成果:
- 拟议的双光谱分析和时空框架在无人机识别方面表现出了卓越的性能.
- 与现有方法相比,准确度提高了6.78%至13.89%.
- 有效地捕获复杂的信号特征和时间演变,用于准确的分类.
结论:
- 双光谱分析与ResNet18-LSTM网络相结合,为使用射频信号的无人机识别提供了强大的方法.
- 该方法显著提高了识别精度,解决了无人机扩散带来的挑战.
- 该框架为安全可靠的无人机监控提供了一个有希望的解决方案.
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