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无人机的无线电频率信号识别基于复杂估值的卷积神经网络

Yibo Xin¹, Junsheng Mu¹, Xiaojun Jing¹

¹School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)

|January 28, 2026

概括

本研究介绍了一种复杂值的卷积神经网络 (CV-CNN),用于识别使用射频 (RF) 信号的无人机. 与传统方法相比,CV-CNN在低信号噪声比 (SNR) 条件下显著提高了识别精度.

科学领域:

人工智能的人工智能
信号处理信号处理
航空航天工程航空航天工程

背景情况:

由于技术的快速进步,无人机识别非常重要.
传统的实值CNN (RV-CNN) 由于丢弃的相位信息,与无线电频率 (RF) 光谱相扎,特别是在信号噪声比 (SNR) 低的环境中.
现有的方法在噪音条件下经常表现出降低的性能.

研究的目的:

为无人机开发一种强大的基于射频的识别方法,克服在低SNR条件下RV-CNNs的局限性.
提出和评估一个复杂值的CNN (CV-CNN),有效地利用来自射频信号的数量和相位信息.
调查建筑选择对CV-CNN性能的影响,并确定稳定性限制.

主要方法:

设计了一个复杂值的CNN (CV-CNN),利用射频谱的复杂表示 (对数PSD作为实部分,索贝尔边缘检测作为虚构部分).
采用真正的复杂卷曲来融合大小和结构线索,提高噪声弹性.
进行了全面的废弃实验,以分析超参数灵敏度和网络深度限制.

主要成果:

在无噪声条件下,CV-CNN在25类DroneRFa数据集上实现了100.00%的准确性.
在低SNR模式下,CV-CNN表现出卓越的稳定性:在-20dB SNR下达到15.58%的精度 (比双通道RV-CNN高出七倍) 和在-15dB SNR下达到45.86%的精度.
在4-5个网络深度之外观察到性能退化,这表明了关键的稳定性约束.

结论:

拟议的CV-CNN在基于射频的无人机识别方面提供了显著改进的稳定性和抗干扰性能,与实际价值的对应产品相比.
复杂值方法有效地利用射频信号特征,即使在具有挑战性的低SNR环境中也保持高精度.
这些发现强调了CV-CNNs在各种操作条件下可靠识别无人机的潜力.

关键词:

索贝尔边缘检测探测器索贝尔边缘检测废弃的实验废弃的实验复杂值的卷积神经网络的神经网络.无线电频率无线电频率的使用情况.无人驾驶飞行器是一种无人驾驶飞行器.

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