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      Automatic Target Recognition of Aircraft using Inverse Synthetic Aperture Radar

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          Abstract

          Along with the improvement of radar technologies, Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR) has come to be an active research area. SAR/ISAR are radar techniques to generate a two-dimensional high-resolution image of a target. Unlike other similar experiments using Convolutional Neural Networks (CNN) to solve this problem, we utilize an unusual approach that leads to better performance and faster training times. Our CNN uses complex values generated by a simulation to train the network; additionally, we utilize a multi-radar approach to increase the accuracy of the training and testing processes, thus resulting in higher accuracies than the other papers working on SAR/ISAR ATR. We generated our dataset with 7 different aircraft models with a radar simulator we developed called RadarPixel; it is a Windows GUI program implemented using Matlab and Java programming, the simulator is capable of accurately replicating a real SAR/ISAR configurations. Our objective is to utilize our multi-radar technique and determine the optimal number of radars needed to detect and classify targets.

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          Support vector machines for SAR automatic target recognition

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            SAR ATR by a combination of convolutional neural network and support vector machines

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              SAR target recognition based on deep learning

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                Author and article information

                Journal
                13 November 2017
                Article
                1711.04901
                5dc155c2-ca9d-4222-84ed-a2e1d8a5d8b3

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                cs.CV

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