Effective Ground-truthing of Supervised Machine Learning for Drone Classification

Sim, J., Jahangir, M., Fioranelli, F. , Baker, C. and Dale, H. (2019) Effective Ground-truthing of Supervised Machine Learning for Drone Classification. In: IEEE International Radar Conference, Toulon, France, 23-27 Sept 2019, ISBN 9781728126609 (doi: 10.1109/RADAR41533.2019.171322)

Abstract

It has already been shown that multibeam staring radar is able to detect and track low observable targets such as drones due to its high sensitivity [1]. Due to this level of sensitivity, targets that have a similar RCS to drones are also detected and tracked. These are predominantly birds. Birds and drones are similar in several ways such as flight altitude, velocity and manoeuvrability [2] such that discrimination between them is challenging. Hence, there is a need to look for high performing methods of classification, for example, machine learning. Supervised training of machine learning classifiers requires accurately labelled training data. For control targets, such as drones, truth data from the on-board GPS logging can be used for data labelling. However, opportune bird targets require a separate data collection method that enables association with the radar output for a classifier to be effectively trained. This paper shows a method of collecting and displaying ground-truth for small targets onto GoogleEarth so that the radar data can be appropriately used to create accurate training data for a machine learning, drone and bird classifier. Results of classification performance are presented showing high performance that is aided by the availability of more effective truth data.

• Item Type: Conference Proceedings
• Additional Information: This work was part funded by the SESAR Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement 763719.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Fioranelli, Dr Francesco
• Authors: Sim, J., Jahangir, M., Fioranelli, F., Baker, C., and Dale, H.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISSN: 2640-7736
• ISBN: 9781728126609
• Published Online: 27 April 2020
• Copyright Holders: Copyright © 2019 IEEE
• First Published: First published in 2019 International Radar Conference (RADAR)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy