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Multistatic radar classification of armed vs unarmed personnel using neural networks

Patel, JS; Fioranelli, F; Ritchie, M; Griffiths, H; (2018) Multistatic radar classification of armed vs unarmed personnel using neural networks. Evolving Systems , 9 (2) pp. 135-144. 10.1007/s12530-017-9208-6. Green open access

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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.

Type: Article
Title: Multistatic radar classification of armed vs unarmed personnel using neural networks
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s12530-017-9208-6
Publisher version: https://doi.org/10.1007/s12530-017-9208-6
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Multistatic Radar, Classification, Deep Neural Networks, Auto-Encoders, Micro-Doppler, Data Ensembling
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10051757
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