Radar Drone Detection: Drone RCS Analysis
S. Abnaamar (TU Delft - Electrical Engineering, Mathematics and Computer Science)
E.I. Özdal (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Alexander Yarovoy – Mentor (Microwave Sensing, Signals & Systems)
N.C. Kruse – Mentor (Microwave Sensing, Signals & Systems)
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Abstract
The increasing accessibility of consumer drones raises safety and security risks, emphasising the necessity of reliable drone detection systems. This thesis characterises the radar cross-section of two commercial drones at 24\,GHz frequency: the DJI Air 3S and DJI Neo, forming part of a broader drone detection and tracking system using frequency-modulated continuous wave radar. Measurements were performed in the Delft University Chamber for Antenna Tests anechoic chamber across a range of azimuth and elevation angles, using two different-bandwidth radar configurations. Here background subtraction and range-gating was utilised to process the results. The higher bandwidth dataset yielded an improved transmit-receive isolation and was prioritised for statistical modelling. Elevation angle configurations were pooled into a combined dataset, due to being considered a non-significant factor in forming the radar cross-section distribution. Five candidate models were compared, including the Swerling I and III target models, the gamma, Weibull, and log-normal distributions. Model selection was based on goodness-of-fit tests including Kolmogorov-Smirnov and Pearson chi-squared tests, along with the Akaike and Bayesian information criteria. Swerling III was deemed the optimal model for the DJI Air 3S, due to statistically performing the best and also being physically grounded; Swerling III captures a dominant scatterer with smaller adjacent scatterers, where the DJI Air 3S has a main body and smaller, protruding propeller arms. For the DJI Neo, the Weibull distribution was selected, while being statistically tied with Swerling III, as its shape parameter provides a measure for radar cross-section fluctuation behaviour, and the drone's compact geometry is insufficiently consistent with the physical implications of Swerling III.