IEICE TRANSACTIONS on Communications
Radar Emitter Identification Based on Auto-Correlation Function and Bispectrum via Convolutional Neural Network
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E104-B No.12 pp.1506-1513
- Publication Date
- 2021/12/01
- Publicized
- 2021/06/10
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2021EBP3035
- Type of Manuscript
- PAPER
- Category
- Fundamental Theories for Communications
Authors
Zhiling XIAO
the Nanjing Research Institute of Electronics Technology
Zhenya YAN
the Nanjing Research Institute of Electronics Technology
Keyword
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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},}
Copy
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 -
English
