Open AccessSynthetic aperture radar automatic target classification processing concept
Author(s) -
Woollard M.,
Ban A.,
Ritchie M.,
Griffiths H.
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.2389
Subject(s) - automatic target recognition , synthetic aperture radar , computer science , convolutional neural network , artificial intelligence , classifier (uml) , pipeline (software) , inverse synthetic aperture radar , radar imaging , radar , bistatic radar , artificial neural network , computer vision , fidelity , target acquisition , remote sensing , telecommunications , geology , programming language
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.