Primary Signal Suppression Based on Synchrosqueezed Wavelet Transform

被引：0
|
作者：
Wu L. [1 ]
Niu J. [1 ]
Wang Z. [2 ]
He S. [1 ]
Zhao Y. [1 ]
机构：
[1] School of Electronics & Information Engineering, Harbin Institute of Technology, Harbin
[2] The 29th Research Institute of China Electronics Technology Group Corporation, Chengdu
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2020年 / 42卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Feature extraction; Primary signal suppression; Specific Emitter Identification(SEI); Synchrosqueezed wavelet transform;
D O I：
10.11999/JEIT30_190650
中图分类号：
学科分类号：
摘要：
In Specific Emitter Identification (SEI), the stability of individual features and final correct identification rate are always declined due to the influence of the primary signal with high energy on the individual features. To solve the problem above, a primary signal suppression algorithm based on synchrosqueezed wavelet transform is exploited for specific emitter identification in this paper. Firstly, a denoising method based on stationary wavelet transform is applied to preprocess the noised signal; Then, the detection and suppression of the primary signal from time-frequency distribution are developed, where root mean square error and Pearson correlation coefficient are used as numerical indicators to measure the effectiveness of the proposed primary signal suppression algorithm; Finally, a feature extraction based on box-counting dimension and a classification based on support vector machine are exploited to verify the identification performance. The simulation results show that the correct identification rate of SEI using the proposed primary signal suppression outperforms the conventional SEI with 10%, which proves the practical improvement of the proposed primary signal suppression algorithm on specific emitter identification.
引用
收藏
页码：2045 / 2052
页数：7
相关论文
共 21 条
  • [1] WANG Xuebao, HUANG Gaoming, ZHOU Zhiwen, Et al., Radar emitter recognition based on the short time fourier transform and convolutional neural networks, The 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, pp. 1-5, (2017)
  • [2] LIANG Kaiqiang, HUANG Zhen, HU Dexiu, Et al., An individual emitter recognition method combining bispectrum with wavelet entropy, 2015 IEEE International Conference on Progress in Informatics and Computing, pp. 206-210, (2015)
  • [3] GUO Haizhao, ZHANG Xiaonu, YANG Libo, Et al., Improved fisher linear discriminant analysis for feature extraction of unintentional modulation on pulse by combining ambiguity function with wavelet transform, IET International Radar Conference 2015, pp. 1-4, (2015)
  • [4] LI Yibing, GE Juan, LIN Yun, Et al., Radar emitter signal recognition based on multi-scale wavelet entropy and feature weighting, Journal of Central South University, 21, 11, pp. 4254-4260, (2014)
  • [5] CAO Yinping, GUO Lu, Application of wavelet analysis based on MATLAB in signal denoising, Information Recording Materials, 19, 7, pp. 85-87, (2018)
  • [6] DUDCZYK J, KAWALEC A., Fractal features of specific emitter identification, Acta Physica Polonica A, 124, 2, pp. 406-409, (2013)
  • [7] DUDCZYK J, KAWALEC A., Identification of emitter sources in the aspect of their fractal features, Bulletin of the Polish Academy of Sciences:Technical Sciences, 61, 3, pp. 623-628, (2013)
  • [8] WU Xiaopo, SHI Yangming, MENG Weibo, Et al., Specific emitter identification for satellite communication using probabilistic neural networks, International Journal of Satellite Communications and Networking, 37, 3, pp. 283-291, (2019)
  • [9] WANG Huanhuan, ZHANG Tao, MENG Fanyu, Specific emitter identification based on time-frequency domain characteristic, Journal of Information Engineering University, 19, 1, pp. 23-29, (2018)
  • [10] WANG Huanhuan, ZHNAG Tao, Specific emitter identification based on fractal and wavelet theories, The 2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference, pp. 1613-1617, (2017)
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