Convolutional Neural Networks for Robust Classification of Drones

Abstract

In order to be effective, radar drone surveillance systems need to be able to discriminate between birds and drones. In this work, convolutional neural networks (CNNs) are used to distinguish between bird and drone spectrograms, where the classifier is tested on real, low signal to background ratio (SBR) data obtained using an L-band staring radar. This allows for a better understanding of the classifier's ability to generalise against new models of drone and new clutter environments. This work highlights the importance of SBR for drone surveillance, placing limits on the size of drone that can be reliably classified, as well as range from the radar.