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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183430.pdf - Accepted Version 2MB |
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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