The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo propõe aplicar a função de autocorrelação (ACF), análise bispectral e redes neurais convolucionais (CNN) para implementar a identificação de emissor de radar (REI) com base em recursos intrapulso. Neste trabalho, combinamos ACF com bispectro para extração de características de sinal. Primeiro calculamos o ACF de cada sinal emissor, depois o biespectro do ACF e obtemos os espectrogramas. As imagens do espectro são tomadas como mapas de características dos emissores de radar e inseridas no classificador CNN para realizar a identificação automática. Simulamos amostras de sinais de diferentes tipos de modulação em experimentos. Também consideramos o método de extração de características diretamente usando análise biespectral para comparação. Os resultados da simulação demonstram que, combinando ACF com análise biespectral, o esquema proposto pode atingir maior robustez ao ruído, os espectrogramas de nossa abordagem têm características mais pronunciadas e nossa abordagem pode alcançar melhor desempenho de identificação em baixas relações sinal-ruído.
Zhiling XIAO
the Nanjing Research Institute of Electronics Technology
Zhenya YAN
the Nanjing Research Institute of Electronics Technology
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Zhiling XIAO, Zhenya YAN, "Radar Emitter Identification Based on Auto-Correlation Function and Bispectrum via Convolutional Neural Network" in IEICE TRANSACTIONS on Communications,
vol. E104-B, no. 12, pp. 1506-1513, December 2021, doi: 10.1587/transcom.2021EBP3035.
Abstract: This article proposes to apply the auto-correlation function (ACF), bispectrum analysis, and convolutional neural networks (CNN) to implement radar emitter identification (REI) based on intrapulse features. In this work, we combine ACF with bispectrum for signal feature extraction. We first calculate the ACF of each emitter signal, and then the bispectrum of the ACF and obtain the spectrograms. The spectrum images are taken as the feature maps of the radar emitters and fed into the CNN classifier to realize automatic identification. We simulate signal samples of different modulation types in experiments. We also consider the feature extraction method directly using bispectrum analysis for comparison. The simulation results demonstrate that by combining ACF with bispectrum analysis, the proposed scheme can attain stronger robustness to noise, the spectrograms of our approach have more pronounced features, and our approach can achieve better identification performance at low signal-to-noise ratios.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021EBP3035/_p
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@ARTICLE{e104-b_12_1506,
author={Zhiling XIAO, Zhenya YAN, },
journal={IEICE TRANSACTIONS on Communications},
title={Radar Emitter Identification Based on Auto-Correlation Function and Bispectrum via Convolutional Neural Network},
year={2021},
volume={E104-B},
number={12},
pages={1506-1513},
abstract={This article proposes to apply the auto-correlation function (ACF), bispectrum analysis, and convolutional neural networks (CNN) to implement radar emitter identification (REI) based on intrapulse features. In this work, we combine ACF with bispectrum for signal feature extraction. We first calculate the ACF of each emitter signal, and then the bispectrum of the ACF and obtain the spectrograms. The spectrum images are taken as the feature maps of the radar emitters and fed into the CNN classifier to realize automatic identification. We simulate signal samples of different modulation types in experiments. We also consider the feature extraction method directly using bispectrum analysis for comparison. The simulation results demonstrate that by combining ACF with bispectrum analysis, the proposed scheme can attain stronger robustness to noise, the spectrograms of our approach have more pronounced features, and our approach can achieve better identification performance at low signal-to-noise ratios.},
keywords={},
doi={10.1587/transcom.2021EBP3035},
ISSN={1745-1345},
month={December},}
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TY - JOUR
TI - Radar Emitter Identification Based on Auto-Correlation Function and Bispectrum via Convolutional Neural Network
T2 - IEICE TRANSACTIONS on Communications
SP - 1506
EP - 1513
AU - Zhiling XIAO
AU - Zhenya YAN
PY - 2021
DO - 10.1587/transcom.2021EBP3035
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E104-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2021
AB - This article proposes to apply the auto-correlation function (ACF), bispectrum analysis, and convolutional neural networks (CNN) to implement radar emitter identification (REI) based on intrapulse features. In this work, we combine ACF with bispectrum for signal feature extraction. We first calculate the ACF of each emitter signal, and then the bispectrum of the ACF and obtain the spectrograms. The spectrum images are taken as the feature maps of the radar emitters and fed into the CNN classifier to realize automatic identification. We simulate signal samples of different modulation types in experiments. We also consider the feature extraction method directly using bispectrum analysis for comparison. The simulation results demonstrate that by combining ACF with bispectrum analysis, the proposed scheme can attain stronger robustness to noise, the spectrograms of our approach have more pronounced features, and our approach can achieve better identification performance at low signal-to-noise ratios.
ER -