西华大学学报（自然科学版）2026，Vol.45Issue(3)：93-100,8.DOI:10.12198/j.issn.1673-159X.5593
基于分帧STFT和ShuffleNetV2的无人机识别
Drone Recognition Based on Frame Division STFT and ShuffleNetV2
摘要
Abstract
Individual identification of drones is an important aspect of drone supervision.This article proposes a method for extracting drone RF fingerprint features based on frame division short-time Fourier transform and combined it with ShuffleNetV2 for drone individual recognition.Compared to the short-time Fourier transform,the segmented short-time Fourier transform uses information entropy to preprocess the data of unmanned aerial vehicle RF signals into frames,and then extracts the time-frequency characteristics of the signal through the short-time Fourier transform.Frame based preprocessing effectively improves the problem of local feature instability caused by time-varying or sudden changes in drone signals.This article uses publicly available datasets for simulation verification and builds an experimental platform for experi-mental verification.The proposed algorithm improves the recognition rate by more than 4%compared to the algorithm without preprocessing.关键词
无人机射频指纹/分类识别/短时傅里叶变换/神经网络Key words
drone RF fingerprint/classification recognition/short-time Fourier transform/neural network分类
信息技术与安全科学引用本文复制引用
何跃华,徐余..基于分帧STFT和ShuffleNetV2的无人机识别[J].西华大学学报（自然科学版）,2026,45(3):93-100,8.
