Using micro-doppler signatures is an effective way to classify different types of UAVs, as well as other airborne objects such as birds. To generate signatures for drones, radar measurements are needed; however, these measurements are limited to the types of available drones, the radar parameters, the targets’ range, and the environments in which these measurements are conducted. In this paper, a new method for generating signature datasets is introduced. The method uses full-wave electromagnetic simulation software. Using this method, radar drones’ datasets can be generated using different types, sizes, drone materials, radar parameters, detected range, targets speed, and rotor RPM for rotary drones. A 77 GHz modeled FMCW radar is used to create dataset for classification purposes. Finally, a Convolutional Neural Network (CNN) algorithm is used to classify five types of drones. Based on the results, the classification of the drones is found to exceed 97% accuracy.

Range-Doppler images are widely used to classify different types of UAVs because each UAV has a unique range-doppler signature. However, a drone’s range-doppler signature depends on its movement mechanism. This is why the classifier accuracy would be degraded if the effect of the mechanical control system wasn’t taken into consideration, which may lead to a non-unique signature of a drone while in-flight. In this paper, a full-wave electromagnetic CAD tool is used to investigate the effect of the control systems of a quadcopter and a hexacopter UAVs on their range-doppler signatures. A Mechanical Control-Based Machine Learning (MCML) algorithm is introduced to classify the two UAVs and its accuracy is found to exceed 90%.