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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Artikel ini mencadangkan untuk menggunakan fungsi korelasi auto (ACF), analisis bispektrum dan rangkaian saraf konvolusi (CNN) untuk melaksanakan pengenalan pemancar radar (REI) berdasarkan ciri intrapulse. Dalam kerja ini, kami menggabungkan ACF dengan bispektrum untuk pengekstrakan ciri isyarat. Kami mula-mula mengira ACF bagi setiap isyarat pemancar, dan kemudian bispektrum ACF dan mendapatkan spektrogram. Imej spektrum diambil sebagai peta ciri pemancar radar dan dimasukkan ke dalam pengelas CNN untuk merealisasikan pengenalan automatik. Kami mensimulasikan sampel isyarat jenis modulasi yang berbeza dalam eksperimen. Kami juga mempertimbangkan kaedah pengekstrakan ciri secara langsung menggunakan analisis bispektrum untuk perbandingan. Keputusan simulasi menunjukkan bahawa dengan menggabungkan ACF dengan analisis bispektrum, skema yang dicadangkan boleh mencapai keteguhan yang lebih kuat kepada hingar, spektrogram pendekatan kami mempunyai ciri yang lebih ketara, dan pendekatan kami boleh mencapai prestasi pengenalan yang lebih baik pada nisbah isyarat-ke-bunyi yang rendah.
Zhiling XIAO
the Nanjing Research Institute of Electronics Technology
Zhenya YAN
the Nanjing Research Institute of Electronics Technology
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
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
Salinan
@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},}
Salinan
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 -