Micro-Motion Feature Analysis and Extraction of UAV Based on SET-CFD

Accurately estimating the micro-motion parameters of unmanned aerial vehicles (UAVs) is crucial for UAV identification and classification. This paper addresses the issue of extracting UAV micro-motion features and proposes a high-precision micro-motion feature extraction algorithm that combines the synchroextracting transform (SET) and cadence frequency distribution (CFD). A echo signal model of rotorcraft UAVs is established, and the Doppler characteristics of rotorcraft UAVs are analyzed. A high-resolution time-frequency analysis method based on SET is proposed, which, combined with CFD, extracts the micro-motion features of UAVs to estimate the parameters of blade rotational speed and blade length, and conducts simulation experiments on the extraction of micro-motion parameters of UAVs. The results show that compared to traditional methods, this approach improves the estimation accuracy of UAV's blade rotational speed by 2.31% and the estimation accuracy of UAV's blade length by 13.7%.