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AuthorYildirim A.
AuthorKiranyaz, Mustafa Serkan
Available date2022-04-26T12:31:21Z
Publication Date2020
Publication NameIEEE Access
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ACCESS.2020.3027472
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85094183494&doi=10.1109%2fACCESS.2020.3027472&partnerID=40&md5=66427245d91c9cf730347129a1de9c20
URIhttp://hdl.handle.net/10576/30614
AbstractIn this study we analyze the signal classification performances of various classifiers for deterministic signals under the additive White Gaussian Noise (WGN) in a wide range of signal to noise ratio (SNR) levels (-40dB to +20dB). The traditional electronic support measure (ESM) systems require high SNR for radar signal classification. LPI (low probability of intercept) radar signals that are received by ESM systems are usually corrupted by noise. So, we demonstrate through extensive simulations that it is possible to achieve high classification performance at low SNR levels providing that the underlying radar signals are known in advance. MF bank classifier, 1D Convolutional Neural Networks (CNNs) and the minimum distance classifier using spectral-domain features (the skewness, the kurtosis, and the energy of the dominant frequency) have been derived for the radar signal classification and their performances have been compared with each other and with the optimal classifier.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectConvolution
Convolutional neural networks
Higher order statistics
Military electronic countermeasures
Radar signal processing
Signal to noise ratio
White noise
Additive White Gaussian noise
Classification performance
Electronic support measure systems
Extensive simulations
Low probability of intercept
Minimum distance classifiers
Radar signal classifications
Signal classification
Gaussian noise (electronic)
Title1D Convolutional Neural Networks Versus Automatic Classifiers for Known LPI Radar Signals under White Gaussian Noise
TypeArticle
Pagination180534-180543
Volume Number8
dc.accessType Abstract Only


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