ABSTRACT
With the continuous development of modern electronic warfare technology, the enemy radar signals captured in complex environments usually have very few useful signals. In order to improve the recognition accuracy and anti-noise performance of radar emitter signal(RES), In this paper, an RES identification method based on variational mode decomposition (VMD) and improved ant lion optimization (IALO) is proposed to optimize support vector machine(SVM) parameters. Firstly, the VMD algorithm is used to decompose the radar signal into six intrinsic mode functions (IMF). Correlation coefficients were used to distinguish correlative modes and uncorrelative modes, the dominant component of noise was denoised locally, and the information dimension(DI), fractal box dimension(DF) and weighted percolation entropy(WPE) were extracted from the reconstructed signals for feature fusion to form three-dimensional feature vectors. Finally, the SVM with optimized IALO parameters is used to identify RES. The simulation results show that the method can still get a high recognition rate under the condition of low signal-to-noise ratio(SNR), when the SNR is not less than 0dB, the recognition rate can reaches 100%, and the method has a strong anti-noise performance.
- P. G. Lederer. Electronic Intelligence: The Interception of Radar Signals.[J]. The Aeronautical Journal (1968),1986,90(891).Google Scholar
- Yihan Xiao,Wenjian Liu,Lipeng Gao. Radar Signal Recognition Based on Transfer Learning and Feature Fusion[J]. Mobile Networks and Applications: The Journal of SPECIAL ISSUES on Mobility of Systems, Users, Data and Computing,2020,25(4).Google Scholar
- L. E. Langley, "Specific emitter identification (SEI) and classical parameter fusion technology," Proceedings of WESCON '93, San Francisco, CA, USA, 1993, pp. 377-381.doi: 10.1109/WESCON.1993.488465Google Scholar
- Ru Cao,Jiuwen Cao,Jian-ping Mei,Chun Yin,Xuegang Huang. Radar emitter identification with bispectrum and hierarchical extreme learning machine[J]. Multimedia Tools and Applications,2019,78(20).Google Scholar
- L. B. Yang, S. S. Zhang and B. Xiao, "Radar emitter signal recognition based on time-frequency analysis," IET International Radar Conference 2013, Xi'an, 2013, pp. 1-4.Google Scholar
- L. Wu, L. Yang and Y. Yuan, "A Recognition Method for Radar Emitter Signals Based on Deep Belief Network and Ambiguity Function Matrix Singular Value Vectors," 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2021, pp. 381-386, doi: 10.1109/IAEAC50856.2021.9390661.Google Scholar
- J. Zhang, F. Wang, O. A. Dobre and Z. Zhong, "Specific Emitter Identification via Hilbert–Huang Transform in Single-Hop and Relaying Scenarios," in IEEE Transactions on Information Forensics and Security, vol. 11, no. 6, pp. 1192-1205, June 2016, doi: 10.1109/TIFS.2016.2520908.Google ScholarDigital Library
- Z. Shang-yue, L. Yuan-yuan and Y. Gong-liu, "EMD interval thresholding denoising based on correlation coefficient to select relevant modes," 2015 34th Chinese Control Conference (CCC), 2015, pp. 4801-4806, doi: 10.1109/ChiCC.2015.7260382.Google Scholar
- Chunyun Song, Jianmin Xu and Yi Zhan, "A method for specific emitter identification based on empirical mode decomposition," 2010 IEEE International Conference on Wireless Communications, Networking and Information Security, 2010, pp. 54-57, doi: 10.1109/WCINS.2010.5541885.Google Scholar
- K. Dragomiretskiy and D. Zosso, "Variational Mode Decomposition," in IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531-544, Feb.1, 2014, doi: 10.1109/TSP.2013.2288675.Google ScholarDigital Library
- U. Satija, N. Trivedi, G. Biswal and B. Ramkumar, "Specific Emitter Identification Based on Variational Mode Decomposition and Spectral Features in Single Hop and Relaying Scenarios," in IEEE Transactions on Information Forensics and Security, vol. 14, no. 3, pp. 581-591, March 2019, doi: 10.1109/TIFS.2018.2855665.Google ScholarCross Ref
- A. Komaty, A. Boudraa, B. Augier and D. Daré-Emzivat, "EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs," in IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 1, pp. 27-34, Jan. 2014, doi: 10.1109/TIM.2013.2275243.Google ScholarCross Ref
- Beck S B M, Curren M D, Sims N D. Pipeline system identification through cross correlation analysis ༻J༽.Process Mechanical Engineering, 2002, 21( 6) : 715 − 723.Google Scholar
- Seyedali Mirjalili. The Ant Lion Optimizer[J]. Advances in Engineering Software,2015,83.Google Scholar
Index Terms
- Radiation emitter signal recognition based on VMD and IALO-SVM
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