Aimed at the deficiency of conventional parameter-level methods in radar specific emitter identification (SEI), which heavily relies on empirical experience and cannot adapt to the waveform change, a novel algorithm is proposed to extract specific features and identify in Hilbert-Huang transform domain. Firstly, 2-dimensional physical representation of emitter is formed with Hilbert-Huang transform (HHT). Based on this, 4 types of multi-view features are constructed, and the feature space is spanned by elaborating the extraction. Principal components, between-class similarity, spectrum entropy, and deep architecture are used to describe the subtle features. Finally, support vector machine (SVM) is selected as the classifier to realize identification to alleviate the small sample problem. Experimental results show that the proposed algorithm realizes specific identification using 4 intentional modulations of simulated data. The selected 4 types of unintentional representations are feasible to discriminate identical emitters. Additionally, the proposed algorithm obtains higher accuracy than typical parameter-level methods in the signal-to-noise ratio (SNR) range [0, 20] dB.
"Specific Emitter Identification via Feature Extraction in Hilbert-Huang Transform Domain," Progress In Electromagnetics Research M,
Vol. 82, 117-127, 2019. doi:10.2528/PIERM19022502
1. Ru, X. H., Z. Liu, W. L. Jiang, et al. "Recognition performance analysis of instantaneous phase and its transformed features for radar emitter identification," IET Radar, Sonar and Navigation, Vol. 10, No. 5, 945-952, 2016. doi:10.1049/iet-rsn.2014.0512
2. Han, T. and Y. Y. Zhou, "Intuitive systemic models and intrinsic features for radar-specific emitter identification," Foundations and Practical Applications of Cognitive Systems and Information Processing, Vol. 215, 153-160, 2014. doi:10.1007/978-3-642-37835-5_14
3. Conning, M. and F. Potgieter, "Analysis of measured radar data for specific emitter identification," IEEE Radar Conference, 35-38, IEEE Press, New York, 2010.
4. Ru, X. H., Z. T. Huang, Z. Liu, et al. "Frequency-domain distribution and bandwidth of unintentional modulation on pulse," Electronics Letters, Vol. 52, No. 22, 1853-1855, 2016. doi:10.1049/el.2016.0733
5. Ru, X. H., Z. Liu, Z. T. Huang, et al. "Evaluation of unintentional modulation for pulse compression signals based on spectrum asymmetry," IET Radar, Sonar and Navigation, Vol. 11, No. 4, 656-663, 2017. doi:10.1049/iet-rsn.2016.0248
6. Ye, H., Z. Liu, and W. Jiang, "Comparison of unintentional frequency and phase modulation features for specific emitter identification," Electronics Letters, Vol. 48, No. 14, 875-877, 2012. doi:10.1049/el.2012.0831
7. Aubry, A., A. Bazzoni, V. Carotenuto, et al. "Cumulants-based radar specific emitter identification," 2011 International Workshop on Information Forensics and Security, 1-6, IEEE Press, New York, 2011.
8. Ren, M. Q., J. Y. Cai, Y. Q. Zhu, et al. "Radar signal feature extraction based on wavelet ridge and high order spectra analysis," IET International Radar Conference, 1-5, IEEE Press, New York, 2009.
9. Liang, K. Q., Z. Huang, D. X. Hu, et al. "An individual emitter recognition method combining bispectrum with wavelet entropy," International Conference on Progress in Informatics and Computing, 206-210, IEEE Press, New York, 2015.
10. Ding, L. D., S. L. Wang, F. G. Wang, et al. "Specific emitter identification via convolutional neural networks," IEEE Communications Letters, Vol. 22, No. 12, 2591-2594, 2018. doi:10.1109/LCOMM.2018.2871465
11. Kang, N. X., M. H. He, J. Han, et al. "Radar emitter fingerprint recognition based on bispectrum and SURF feature," 2016 CIE International Conference on Radar, 1-5, Guangzhou, 2016.
12. Zhang, J. W., F. G. Wang, O. A. Dodre, et al. "Specific emitter identification via Hilbert-Huang transform in single-hop and relaying scenarios," IEEE Transactions on Information Forensics and Security, Vol. 11, No. 6, 1192-1205, 2016. doi:10.1109/TIFS.2016.2520908
13. Yuan, Y. J., Z. T. Huang, H. Wu, et al. "Specific emitter identification based on Hilbert-Huang transform-based time-frequency-energy distribution features," IET Communications, Vol. 8, No. 13, 2404-2412, 2014. doi:10.1049/iet-com.2013.0865
14. Hui, X. N., S. L. Zheng, J. H. Zhou, et al. "Hilbert-Huang transform time-frequency analysis in φ-OTDR distributed sensor," IEEE Photonics Technology Letters, Vol. 26, No. 23, 2403-2406, 2014. doi:10.1109/LPT.2014.2358262
15. Han, J., T. Zhang, Z. Y. Qiu, et al. "Communication emitter individual identification via 3D-Hilbert energy spectrum-based multiscale segmentation features," International Journal Communication System, Vol. 32, No. 1, e3833, 2019, https://doi.org/10.1002/dac.3833. doi:10.1002/dac.3833
16. Zhu, B. and W. D. Jin, "Feature extraction of radar emitter signal based on wavelet packet and EMD," Information Engineering and Applications, Vol. 7, No. 6, 198-205, 2012.
17. Liang, J. H., Z. T. Huang, and Z. W. Li, "Method of empirical mode decomposition in specific emitter identification," Wireless Personal Communications, Vol. 96, No. 3, 2447-2461, 2017. doi:10.1007/s11277-017-4306-0
18. Guo, Q., P. L. Nan, X. Y. Zhang, et al. "Recognition of radar emitter signals based on SVD and AF main ridge slice," Journal of Communications and Networks, Vol. 17, No. 5, 491-498, 2015. doi:10.1109/JCN.2015.000087
19. Zhang, G. X., H. N. Rong, L. Z. Hu, et al. "Entropy feature extraction approach for radar emitter signals," International Conference on Intelligent Mechatronics and Automation, 621-625, IEEE Press, New York, 2004.
20. Zhou, Z. W., G. M. Huang, H. Y. Chen, et al. "Automatic radar waveform recognition based on deep convolutional denoising auto-encoders," Circuits, Systems, and Signal Processing, Vol. 37, No. 9, 4034-4048, 2018. doi:10.1007/s00034-018-0757-0
21. Chang, C. and C. Lin, "LIBSVM: A library for support vector machines,", http://www.csie.ntu.edu.tw/∼cjlin, 2001.