Small Unmanned Aerial Vehicle Identification using Radar

Abstract

Attacks using drones are increasing, war zones see more and more use of drones and civilian areas are threatened by cheap commercial drones. In order to prevent drone attacks, they have to be detected; identifiedand neutralized. Faster identification results in more time to respond, making identification vital. This thesisuses radar to identify drone threats using behavioral history. The basis for identification is created by flyingexperiments around critical infrastructure whilst recording the movement using radar. Subsequently multi-ple identification algorithms are compared to determine the fastest and most accurate way of identification.We determined distance to critical infrastructure and degrees scouted around critical infrastructure are themost important features. Next to that, the random forest achieves 96% accuracy, decreasing to 86% whenchallenged. The decision tree scores 94% accuracy, but due to its explanatory nature it becomes the desiredalgorithm. Understanding the basis for action is essential in neutralizing drone threats, making decision treesthe preferred method of identification.