Synthetic aperture radar automatic target classification processing concept. Issue 24 (1st November 2019)

• Record Type: Journal Article
• Authors: Woollard, M.; Bannon, A.; Ritchie, M.; Griffiths, H.
• Is Part Of: Electronics letters. Volume 55:Issue 24(2019)
• Page Start: 1301
• Page End: 1303
• Publication Date: 2019-11-01
• Subjects: CAD -- image classification -- radar imaging -- synthetic aperture radar -- military radar -- convolutional neural nets -- learning (artificial intelligence) -- radar computing; synthetic aperture radar automatic target classification processing concept -- open source tools -- high fidelity synthetic aperture radar simulations -- ground vehicles -- RaySAR open‐source model -- monostatic geometries -- bistatic geometries -- input CAD models -- military vehicles -- civilian vehicles -- SAR imagery -- convolutional neural network classifier -- automatic target recognition technique -- neural network classifier -- ATR technique -- SAR simulations -- CNN classifier; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2019.2389
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : 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 is presented in this Letter. 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 convolutional neural network (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.