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

Abstract

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.

Study on the micro-Doppler separation method of bistatic inverse synthetic aperture radar targets with rotating parts based on complex variational mode decomposition

“…During the coherent accumulation of Bi-ISAR imaging, the rotating angle of the main target part is small, and the Doppler frequency caused by its radar echo is low. In contrast, the Doppler frequency caused by the radar echo can be significantly broadened due to the fast rotation of the micromotion part of the target 26 , 27 . After decomposing the time-domain echo signal by CVMD, the signal can be decomposed into a series of mode functions distributed from low to high frequencies.…”

Micro-Doppler Signal Representation for Drone Classification by Deep Learning

“…The most common is a set of contiguous micro-Doppler profiles called spectrograms obtained thanks to STFT (Short Time Fourier Transform). However, other formats such as CP (Cepstrum), CVD (Cadence Velocity Diagram) [6], [14], [15] or even directly the time signal x(t) are also investigated.…”

An Investigation of Rotary Drone HERM Line Spectrum under Manoeuvering Conditions

“…Micro-Doppler radar signatures are studied using time-frequency analysis, and most commonly using the Short Time Fourier Transform (STFT) [ 2 , 3 , 5 , 6 , 11 , 12 , 13 , 14 , 15 ]. Depending on the STFT window length, two different types of spectrograms are obtained.…”