Stepped frequency radar target recognition using 1D-CNN

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Abstract
  • Real radar returns from four small scale commercial aircraft models are used to train and test a convolutional neural network target recognition system. Many target recognition systems convert the one dimensional stepped-frequency features into two-dimensional using tools such as spectrograms and scalograms, and thereby utilize a two-dimensional CNN. In this paper, a one-dimensional convolutional neural net is used. The unknown target's azimuth position may be known completely or within a certain range. The recognition performance is compared with that of an optimal Bayesian classifier assuming complete statistical knowledge. A discussion of the advantages and disadvantages of using 1D-CNN is presented.
TitleStepped frequency radar target recognition using 1D-CNN
CreatorJouny, Ismail
PublisherProceedings of the SPIE
Academic DepartmentElectrical & Computer Engineering
DivisionEngineering
OrganizationLafayette College
Date Issued2022
Date Available2022-11-01
TypeArticle
LanguageEnglish
Keywordtarget recognition
stepped-frequency radar
1D-CNN
LSTM
Bibliographic CitationJouny, I. (2022) “Stepped frequency radar target recognition using 1D-CNN.” Proceedings of the SPIE 12096: 120960C.
Standard IdentifierDOI 10.1117/12.2618613
Handle 10385/kh04dr26x
Permalinkhttp://hdl.handle.net/10385/kh04dr26x
Rights Statement In Copyright In Copyright
Rights HoldersSPIE
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