Small Target Detection in a Radar Surveillance System Using Contractive Autoencoders
Simon Wagner, Winfried Johannes, Denisa Qosja, Stefan Brüggenwirth
Abstract
With the rapid development of Unpiloted Aerial Vehicles (UAVs), also known as drones, in recent years, the need for surveillance systems that are able to detect drones has grown as well. Radar is technology with the potential to fulfill this task and several previous publications show examples of radar detection and classification schemes. The purpose of this paper is related to the detection scheme used in these radars. Most surveillance systems use a background subtraction and a threshold to detect targets. This threshold often depends on a model of the radar noise and the background, which is imperfect by nature. The approach presented here uses a data driven machine learning algorithm that is trained with measured background profiles of the radar and is applied afterwards to the given background for target detection. This scheme can in general be applied to any detection problem in a fixed area, but is shown here with examples from measurements of drones and persons. The results show that the chosen approach gives better detection rates for low false alarm rates with real data than background subtraction.