Fusion of deep representations in multistatic radar networks to counteract the presence of synthetic jamming

Patel, J. , Fioranelli, F. , Ritchie, M. and Griffiths, H. (2019) Fusion of deep representations in multistatic radar networks to counteract the presence of synthetic jamming. IEEE Sensors Journal, 19(15), pp. 6362-6370. (doi:10.1109/JSEN.2019.2909685)

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Abstract

Micro-Doppler signatures are extremely valuable in the classification of a wide range of targets. This paper investigates the effects of jamming on the micro-Doppler classification performance and explores a potential deep topology enabling low-bandwidth data fusion between nodes in a multistatic radar network. The topology is based on an array of three independent deep neural networks (DNNs) functioning cooperatively to achieve joint classification. In addition to this, a further DNN is trained to detect the presence of jamming, and from this, it attempts to remedy the degradation effects in the data fusion process. This is applied to the real experimental data gathered with the multistatic radar system, NetRAD, of a human operating with seven combinations of holding a rifle-like object and a heavy backpack that is slung on their shoulders. The resilience of the proposed network is tested by applying synthetic jamming signals into specific radar nodes and observing the networks' ability to respond to these undesired effects. The results of this are compared with a traditional voting system topology, serving as a convenient baseline for this paper.

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., Fioranelli, F., Ritchie, M., and Griffiths, H.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Sensors Journal
Publisher:IEEE
ISSN:1530-437X
ISSN (Online):1558-1748
Published Online:09 April 2019
Copyright Holders:Copyright © 2019 IEEE.
First Published:First published in IEEE Sensors Journal 19(15):6362-6370
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3015260Intelligent RF Sensing for Fall and Health PredictionFrancesco FioranelliEngineering and Physical Sciences Research Council (EPSRC)EP/R041679/1ENG - Systems Power & Energy