电讯技术2026，Vol.66Issue(8)：1298-1307,10.DOI:10.20079/j.issn.1001-893x.250418003
双骨干轻量抗噪的无人机射频信号识别模型
A Dual-backbone Lightweight and Noise-robust Model for UAV RF Signal Recognition
摘要
Abstract
To address the problems of low recognition accuracy and high model complexity of unmanned aerial vehicle(UAV)radio frequency(RF)signals recognition in complex noise environments,a lightweight heterogeneous dual-backbone convolutional neural network(HDB-CNN)is proposed.This model takes the time-frequency graph generated by short-time Fourier transform(STFT)as input.The efficient convolution backbone adopts channel expansion and residual connections to enhance the capture ability of short-term abrupt features.The densely-connected backbone explores long-term frequency band correlations through layer-by-layer feature reuse and multi-scale pooling.The features of the two backbones are concatenated and fused to achieve complementary time-frequency features,and large-size global average pooling(GAP)is combined to compress parameters and reduce overfitting risk.Compared with MobileNet v3,the model reduces the number of parameters by 57%,enabling its suitability for edge device deployment.In experiments based on the DroneRFa dataset,HDB-CNN improves the average accuracy by 7.8%compared with ResNet50 under Gaussian white noise,and by 8.9%under-9 dB mixed Gaussian white noise and impulse noise.关键词
无人机射频信号识别/异构双骨干网络(HDB-CNN)/轻量级卷积神经网络/抗噪识别Key words
UAV RF signal recognition/heterogeneous dual-backbone convolutional neural network(HDB-CNN)/lightweight CNN/anti-noise identification分类
信息技术与安全科学引用本文复制引用
尹童,伍春,江虹,乔仟,张秋云,曾闵..双骨干轻量抗噪的无人机射频信号识别模型[J].电讯技术,2026,66(8):1298-1307,10.基金项目
国家自然科学基金资助项目(62441111) （62441111）
四川省国际港澳台合作项目(2025YFHZ0199) （2025YFHZ0199）
四川省自然科学基金面上项目(2024NSFSC0476) （2024NSFSC0476）
