Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR

Micro-Doppler feature extraction of unmanned aerial vehicles (UAVs) is important for their identification and classification. Noise and the motion state of the UAV are the main factors that may affect feature extraction and estimation precision of the micro-motion parameters. The spectrum of UAV echoes is reconstructed to strengthen the micro-motion feature and reduce the influence of the noise on the condition of low signal to noise ratio (SNR). Then considering the rotor rate variance of UAV in the complex motion state, the cepstrum method is improved to extract the rotation rate of the UAV, and the blade length can be intensively estimated. The experiment results for the simulation data and measured data show that the reconstruction of the spectrum for the UAV echoes is helpful and the relative mean square root error of the rotating speed and blade length estimated by the proposed method can be improved. However, the computation complexity is higher and the heavier computation burden is required.