Multi-time Frequency Analysis and Classification of a Micro Drone Carrying Payloads using Multistatic Radar

Patel, J. S. , Al-Ameri, C., Fioranelli, F. and Anderson, D. (2019) Multi-time Frequency Analysis and Classification of a Micro Drone Carrying Payloads using Multistatic Radar. Journal of Engineering, 2019(20), pp. 7047-7051. (doi: 10.1049/joe.2019.0551)

Abstract

This article presents an analysis of three multi-domain transformations applied to radar data of a micro-drone operating in an open field, with a payload (between 200 and 600 g) and without a payload. Inferring the presence of a drone attempting to transport a payload beyond its normal operating conditions is a key enabler in prospective low altitude airspace security systems. Two scenarios of operation were explored, the first with the drone hovering and the second with the drone flying. Both were accomplished through real experimental trials, undertaken with the multistatic radar, NetRAD. The images generated as a result of the domain transformations were fed into a pretrained convolutional neural network (CNN), known as AlexNet and were treated as a six-class classification problem. Very promising accuracies were obtained, with on average 95.1% for the case of the drone hovering and 96.6% for the case of the drone flying. The activations that these variety of images triggered within the CNN were then visualised to better understand the specific features that the network was learning and distinguishing between, in order to successfully achieve classification.

• Item Type: Articles
• Additional Information: IET International Radar Conference, Nanjing, China, 17-19 Oct 2018. This research is jointly funded by the College of Engineering (UoG) and by Leonardo Airborne and Space Systems.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Patel, Mr Jarez and Fioranelli, Dr Francesco and Al-Ameri, Mr Caesar and Anderson, Dr David
• Authors: Patel, J. S., Al-Ameri, C., Fioranelli, F., and Anderson, D.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Journal of Engineering
• Publisher: IET
• ISSN: 2051-3305
• ISSN (Online): 2051-3305
• Published Online: 22 August 2019
• Copyright Holders: Copyright © 2020 The Authors
• First Published: First published in Journal of Engineering 2019(20):7047-7051
• Publisher Policy: Reproduced under a Creative Commons license