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    1D Convolutional Neural Networks Versus Automatic Classifiers for Known LPI Radar Signals under White Gaussian Noise

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    Date
    2020
    Author
    Yildirim A.
    Kiranyaz, Mustafa Serkan
    Metadata
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    Abstract
    In 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.
    URI
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094183494&doi=10.1109%2fACCESS.2020.3027472&partnerID=40&md5=66427245d91c9cf730347129a1de9c20
    DOI/handle
    http://dx.doi.org/10.1109/ACCESS.2020.3027472
    http://hdl.handle.net/10576/30614
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    • Electrical Engineering [‎1690‎ items ]

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