Extraction of micro-doppler characteristics of drones using high-resolution time-frequency transforms

The demand for detecting and tracking drones has increased for reasons of surveillance and security. Radar is one of the promising methods in this regard. The recognition and identification of drones using a radar system requires the extraction of their unique micro-Doppler signatures produced by their rotating blades. Because of the blades' rapid rotation speed, difficulties are inherent in visualizing clear micro-Doppler signatures in a conventional joint time-frequency analysis such as the short-time Fourier transform. In this paper, we propose the use of high-resolution transform techniques to visualize the micro-Doppler signatures of drones in a spectrogram. The techniques used include Wigner-Ville distribution, smoothed pseudo-Wigner-Ville distribution, and short-time MUltiple SIgnal Classification (MUSIC) algorithm. In particular, the latter, which had never previously been applied to drones, is suggested to visualize the details of micro-Doppler signatures. We measured three drones using a continuous-wave radar, and performances of these algorithms were compared using data collected from the drones. We could observe that the short-time MUSIC method showed the clearest spectrogram for identifying micro-Doppler signatures. This study can potentially be useful in the field of drone classification.