Multistatic radar classification of armed vs unarmed personnel using neural networks

Patel, J. S. , Fioranelli, F. , Ritchie, M. and Griffiths, H. (2018) Multistatic radar classification of armed vs unarmed personnel using neural networks. Evolving Systems, 9(2), pp. 135-144. (doi: 10.1007/s12530-017-9208-6)

Abstract

This paper investigates an implementation of an array of distributed neural networks, operating together to classify between unarmed and potentially armed personnel in areas under surveillance using ground based radar. Experimental data collected by the University College London (UCL) multistatic radar system NetRAD is analysed. Neural networks are applied to the extracted micro-Doppler data in order to classify between the two scenarios, and accuracy above 98% is demonstrated on the validation data, showing an improvement over methodologies based on classifiers where human intervention is required. The main advantage of using neural networks is the ability to bypass the manual extraction process of handcrafted features from the radar data, where thresholds and parameters need to be tuned by human operators. Different network architectures are explored, from feed-forward networks to stacked auto-encoders, with the advantages of deep topologies being capable of classifying the spectrograms (Doppler-time patterns) directly. Significant parameters concerning the actual deployment of the networks are also investigated, for example the dwell time (i.e. how long the radar needs to focus on a target in order to achieve classification), and the robustness of the networks in classifying data from new people, whose signatures were unseen during the training stage. Finally, a data ensembling technique is also presented which utilises a weighted decision approach, established beforehand, utilising information from all three sensors, and yielding stable classification accuracies of 99% or more, across all monitored zones.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Patel, Mr Jarez and Ritchie, Mr Matthew and Fioranelli, Dr Francesco
• Authors: Patel, J. S., Fioranelli, F., Ritchie, M., and Griffiths, H.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Evolving Systems
• Publisher: Springer
• ISSN: 1868-6478
• ISSN (Online): 1868-6486
• Published Online: 10 November 2017
• Copyright Holders: Copyright © 2017 Springer-Verlag GmbH Germany
• First Published: First published in Evolving Systems 9(2): 135-144
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher