Multi-Frequency Radar Micro-Doppler Based Classification of Micro-Drone Payload Weight - Dhulashia, Dilan; Peters, Nial; Horne, Colin; Beasley, Piers; Ritchie, Matthew (Frontiers in Signal Processing, 2021)
The use of drones for recreational, commercial and military purposes has seen a rapid increase in recent years. The ability of counter-drone detection systems to detect whether a drone is carrying a payload is of strategic importance as this can help determine the potential threat level posed by a detected drone. This paper presents the use of micro-Doppler signatures collected using radar systems operating at three different frequency bands for the classification of carried payload of two different micro-drones performing two different motions. Use of a KNN classifier with six features extracted from micro-Doppler signatures enabled mean payload classification accuracies of 88.01%, 65.78% and 73.53% for data collected at W-band, C-band, and S-band, respectively, when the drone type and motion type is known, and 76.00%, 66.30%, and 73.90%, when the drone type and motion type are unknown. The impact on classification performance of different amounts of situational information is also evaluated in this paper.