UAV Classification using Neural Networks and CAD-generated Radar Datasets

Abstract

<p>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. </p>