{"title":"Deep Learning of Raw Radar Echoes for Target Recognition","authors":"Tian Tian Fan, Che Liu, T. Cui","doi":"10.1109/COMPEM.2018.8496666","DOIUrl":null,"url":null,"abstract":"Synthetic aperture radar (SAR) based classification approaches are commonly used methods for automatic target recognition. However, SAR imaging requires complex two-dimensional matched filtering and interpolation algorithms. In this paper, we propose deep learning technology for automatic target recognition based on raw radar echoes instead of SAR images. A modern convolutional neural network (CNN) model is trained directly by radar-echo training data set, and is evaluated on the testing data set. The experimental results show that the proposed method could achieve high accuracy and efficiency for the target recognition.","PeriodicalId":221352,"journal":{"name":"2018 IEEE International Conference on Computational Electromagnetics (ICCEM)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference on Computational Electromagnetics (ICCEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMPEM.2018.8496666","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Synthetic aperture radar (SAR) based classification approaches are commonly used methods for automatic target recognition. However, SAR imaging requires complex two-dimensional matched filtering and interpolation algorithms. In this paper, we propose deep learning technology for automatic target recognition based on raw radar echoes instead of SAR images. A modern convolutional neural network (CNN) model is trained directly by radar-echo training data set, and is evaluated on the testing data set. The experimental results show that the proposed method could achieve high accuracy and efficiency for the target recognition.