Woollard, M;
Bannon, A;
Ritchie, M;
Griffiths, H;
(2019)
Synthetic aperture radar automatic target classification processing concept.
Electronics Letters
, 55
(24)
pp. 1301-1303.
10.1049/el.2019.2389.
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Abstract
This paper presents a new simulation and processing methodology based on open source tools to produce high fidelity Synthetic Aperture Radar (SAR) simulations of ground vehicles of varying types, as well as analysis of an applied Automatic Target Recognition (ATR) technique. This work is based around the RaySAR open source model and the outputs have been configured for both monostatic and bistatic geometries. Input CAD models of various military and civilian vehicles are used to produce the SAR imagery. This output imagery was then used to train a Tiny You Only Look Once (YOLO) Convolutional Neural Net (CNN) classifier. The classification success of the CNN applied was showed to produce significantly accurate results and the whole pipeline of processing enabled rapid evaluation of potential ATR methods against targets of choice.
| Type: | Article |
|---|---|
| Title: | Synthetic aperture radar automatic target classification processing concept |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1049/el.2019.2389 |
| Publisher version: | http://dx.doi.org/10.1049/el.2019.2389 |
| 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: | Synthetic Aperture Radar, Automatic Target Recognition, Simulation |
| 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/10082235 |
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