DETECTION OF DRONES THROUGH MICRO-DOPPLER EFFECT ANALYSIS
DOI:
https://doi.org/10.31891/2219-9365-2024-80-30Keywords:
quadcopter, DJI Mini 2, micro-Doppler effect, radar cross-section, EM wave scattering, resonanceAbstract
The paper is dedicated to investigation of the micro-Doppler effect in unmanned aerial vehicles (UAVs) based on the DJI Mini 2 quadcopter utilization. While the Doppler effect is widely applied to moving objects, the micro-Doppler effect can manifest in stationary objects with components undergoing micro-motions, such as oscillation, rotation, or vibration. The research demonstrates that the propeller of a quadcopter interacts differently with electromagnetic (EM) waves depending on its orientation relative to the wave.
Using computational modeling in the frequency range of 2 to 30 GHz, the study reveals the presence of resonant interactions between EM waves and the propeller in two frequency bands: (a) 5–12 GHz, where the EM wavelength is commensurate with the propeller’s length, and (b) 18–25 GHz, where the wavelength matches the propeller’s width. Experimental investigations conducted in a certified anechoic chamber within the 2–12 GHz range vividly demonstrate the dependency of the radar cross-section (RCS) values on the propeller's rotational angle. Furthermore, these experiments confirm the resonant nature of the scattering, evidenced by a sharp increase in the RCS spectrum within the 8–11 GHz range.
Dynamic experiments were performed using two types of radar systems: (a) a laboratory-assembled continuous wave (CW) radar and (b) a commercial AWR1642 radar manufactured by National Instruments. The CW radar detected a distinct harmonic component corresponding to the micro-Doppler frequency for a stationary hovering quadcopter. The AWR1642 radar, benefiting from higher resolution, captured micro-motions of the rotating propellers, allowing for Doppler analysis of individual propeller segments, thereby confirming the micro-Doppler effect.
The findings can be applied as an additional tool for detecting UAVs and classifying them based on the temporal or spectral characteristics of the detected signals, which can serve as unique signatures for different UAV types.
