Radar Emitter Identification Based on Novel Time-Frequency Spectrum and Convolutional Neural Network

Radar emitter identification (REI) is significant in both military and civilian application domains. A critical step for REI is signal feature extraction. Most radar emitter signals are non-stationary, and many studies apply time-frequency spectrum features for non-stationary signal analysis in rece...

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Veröffentlicht in:	IEEE communications letters Jg. 25; H. 8; S. 2634 - 2638
Hauptverfasser:	Xiao, Zhiling, Yan, Zhenya
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k -means algorithm Algorithms Artificial neural networks convolutional neural network Emitters Feature extraction Fourier transforms Frequency shift keying Frequency spectrum Gray-scale Military applications Neural networks Performance evaluation Radar Radar emitter identification Signal analysis Signal to noise ratio Spectrogram Spectrograms Spectrum analysis Time-frequency analysis
ISSN:	1089-7798, 1558-2558
Online-Zugang:	Volltext
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