基于分帧STFT和ShuffleNetV2的无人机识别OA

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.