High-Precision UAV Micro-Motion Feature Extraction and Estimation Method Based on Local Maximum Synchroextracting Transform

Accurately estimating the micro-motion parameters of unmanned aerial vehicles (UAVs) is of significant importance for UAV recognition and classification. Traditional time-frequency analysis methods have low resolution and suffer from severe cross-term interference, making it challenging to precisely estimate the micro-motion parameters of UAVs. In order to slove this problem, this paper proposes a high-resolution local maximum synchroextracting transform method(LSET). Firstly, echo data is collected using FMCW, and then the data is subjected to STFT. Next, an estimation of the instantaneous frequency is calculated, and finally, the value of the maximum energy in the time-frequency representation is extracted to retain useful information. The experimental results from the actual data testing show that compare with the traditional time-frequency analysis method, the proposed method reduces the average Renyi entropy by 4.7 dB. A smaller entropy value indicates higher time-frequency resolution and energy concentration. This method improves the average length estimation accuracy of rotating blades by 33.3%.