Multistatic micro-doppler radar feature extraction for classification of unloaded/loaded micro-drones

Ritchie, M., Fioranelli, F. , Borrion, H. and Griffiths, H. (2017) Multistatic micro-doppler radar feature extraction for classification of unloaded/loaded micro-drones. IET Radar, Sonar and Navigation, 11(1), pp. 116-124. (doi:10.1049/iet-rsn.2016.0063)

119563.pdf - Accepted Version



This paper presents the use of micro-Doppler signatures collected by a multistatic radar to detect and discriminate between micro-drones hovering and flying while carrying different payloads, which may be an indication of unusual or potentially hostile activities. Different features have been extracted and tested, namely features related to the Radar Cross Section of the micro-drones, as well as the Singular Value Decomposition (SVD) and centroid of the micro-Doppler signatures. In particular, the added benefit of using multistatic information in comparison with conventional radar is quantified. Classification performance when identifying the weight of the payload that the drone was carrying while hovering was found to be consistently above 96% using the centroid-based features and multistatic information. For the non-hovering scenarios classification results with accuracy above 95% were also demonstrated in preliminary tests in discriminating between three different payload weights.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco
Authors: Ritchie, M., Fioranelli, F., Borrion, H., and Griffiths, H.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:IET Radar, Sonar and Navigation
Publisher:Institution of Engineering and Technology
ISSN (Online):1751-8792
Published Online:24 May 2016
Copyright Holders:Copyright © 2016 Institution of Engineering and Technology
First Published:First published in IET Radar, Sonar and Navigation 11(1): 116-124
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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