The authors acknowledge the funding received by the Army Research Laboratory under Cooperative Agreement Number: W911NF-19-2-0075.Commercial or customised drones with the ability to carry payloads have the potential to cause security threats so the need to accurately detect and identify them with suitable sensors has increased in recent times. Radar sensors are well capable of detecting and classifying a drone by using the unique signatures produced from both the stationary and rotating parts of the target. In this study we have examined the radar signatures of drones carrying different types of payloads which simulate the following three hazardous scenarios: 1) liquid spray, 2) Inertial forces simulating a gun recoil effect, and 3) heavy payloads. The main objective was to model the radar signatures of these scenarios and analyse the characteristic signatures. Two radars, operating at 24 GHz and 94 GHz, have been used to collect data to validate the modelling. The results of the study demonstrate that the payloads produce unique radar return signals, mainly in the Doppler domain, which can be used for robust classification.Publisher PD
