Abstract
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.