Skip to main content
SpringerLink
Log in

Sorting radar signal based on wavelet characteristics of wigner-ville distribution

  • Published:
Journal of Electronics (China)

Abstract

Common sorting method have low sorting rates and is sensitive to the Signal-to-Noise Ratio (SNR), wavelet characteristics of Wigner-Ville distribution are applied to sort unknown complicated radar signal, high sorting accuracy can be got. The Wigner-Ville distribution of received signal is calculated, then it is predigested to two-dimensional characteristics. Using wavelet transformation to extract characteristics from two-dimensional of Wigner-Ville distribution, the best characteristics are selected to be used as sorting parameters. Experiment results demonstrated that the characteristics of eight typical radar emitter signals extracted by this method showed good performance of noise-resistance and clustering at large-scale SNR.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. R. G. Wiley. ELINT: The Interception and Analysis of Radar Signals. Second Edition, Boston, MA, USA, Artech House, 2006, 317–334.

    Google Scholar 

  2. D. J. Nelson. Special purpose correlation functions for improved signal detection and parameter estimation. International Conference on Acoustics, Speech, and Signal Processing, Minneapolis, USA, 1993, 73–76.

    Chapter  Google Scholar 

  3. Liu Xubo and Si Xicai. New method for sorting radar signals. Systems Engineering and Electronics, 32(2010)1, 53–56 (in Chinese). 刘旭波, 司锡才. 雷达信号分选实现的新方法. 系统工程与电子技术, 32(2010)1, 53–56.

    Google Scholar 

  4. Li Hesheng, Han Yu, Cai Yingwu, et al.. Overview of the crucial technology research for radar signal sorting. Systems Engineering and Electronics, 27(2005)12, 2035–2040 (in Chinese). 李合生, 韩宇, 蔡英武, 等. 雷达信号分选关键技术研究综述. 系统工程与电子技术, 27(2005)12, 2035–2040.

    Google Scholar 

  5. Cheng Sheng, Wang Xing, Chu Wei, and Zhang Guanrong. Research on recognition of phased-array radar based on analysis of pulses amplitude. Computer Engineering and Applications, 48(2012)23, 120–124 (in Chinese). 程胜, 王星, 楚维, 张官荣. 基于脉冲幅度特性的相控阵雷达识别技术研究. 计算机工程与应用, 48(2012)23, 120–124.

    Google Scholar 

  6. Wang Shiqiang, Zhang Dengfu, Bi Duyan, and Yong Xiaoju. Novel radar signal sorting method with low complexity. Journal of xidian University, 38(2011)4, 148–153 (in Chinese). 王世强, 张登福, 毕笃彦, 雍霄驹. 一种低复杂度的雷达信号分选方法. 西安电子科技大学学报, 38(2011)4, 148–153.

    Google Scholar 

  7. Wesley A. Gee. Radar warning receiver (RWR) time-coincident pulse data extraction and processing. Proceedings of IEEE National Radar Conference, Atlanta, GA, USA, 2012, 752–757.

    Google Scholar 

  8. Zhang Gexiang, Hu Laizhao, and Jing Weidong. Radar emitter signal recognition based on entropy features. Chinese Journal of Radio Science, 20(2005) 4, 440–445 (in Chinese). 张葛祥, 胡来招, 金炜东. 基于熵特征的雷达辐射源信号识别. 电波科学学报, 20(2005)4, 440–445.

    Google Scholar 

  9. Han Jun, He Minghao, Mao Yan, and Ren Mingqiu. A new method for recognizing radar radiating-source. International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China, 2007, 1665–1668.

    Google Scholar 

  10. Gexiang Zhang, Weidong Jin, and Laizhao Hu. Radar emitter signal recognition based on complexity features. Journal of Southwest Jiaotong University (English Edition), 12(2004)2, 116–122.

    Google Scholar 

  11. Ren Mingqiu and Cai Jinyan. Radar emitter recognition based on wavelet ridge and FSVM. Chinese Journal of Scientific Instrument, 31(2010)6, 1424–1428 (in Chinese). 任明秋, 蔡金燕. 基于小波脊和FSVM 的雷达辐射源识别. 仪器仪表学报, 31(2010)6, 1424–1428.

    Google Scholar 

  12. Guo Qiang, Wang Changhong, and Li Zheng. Support vector clustering and type-entropy based radar signal sorting method. Journal of Xi’an Jiaotong University, 44(2010)8, 63–67 (in Chinese). 国强, 王常虹, 李峥. 支持向量聚类联合类型熵识别的雷达信号分选方法. 西安交通大学学报, 44(2010)8, 63–67.

    MathSciNet  MATH  Google Scholar 

  13. Azeemsha Thacham Poyil. Multi resolution signal analysis using improved Wigner-Ville distribution. Proceedings of 2012 International Conference on Communication, Information and Computing Technology, Mumbai, India, 2012, 56–61.

    Google Scholar 

  14. T. O. Gulum and A. Y. Erdogan. A parameter extraction technique for FMCW radar signals using Wigner-Hough-Radon transform. Proceedings of IEEE National Radar Conference, GA, USA, 2012, 847–852.

    Google Scholar 

  15. Rafael Torres and Edmanuel Torres. Fractional Fourier analysis of random signals and the notion of α-stationarity of the Wigner-Ville distribution. IEEE Transactions on Signal Processing, 61(2013)6, 1555–1560.

    Article  MathSciNet  Google Scholar 

  16. Huangpu Kan, Chen Jianwen, and Lou Shengqiang. Modern Digital Signal Processing. Beijing, Publishing House of Electronics Industry, 2002, 369–384 (in Chinese). 皇甫堪, 陈建文, 楼生强. 现代数字信号处理. 北京, 电子工业出版社, 2003, 369–384

    Google Scholar 

  17. Hu Changhua, Li Guohua, Liu tao, et al.. Systems Analysis and Design Based on MATLAB 6.X-wavelet Analysis. Xi’an, Xidian University Publishing House, 2004, 153–188 (in Chinese). 胡昌华, 李国华, 刘涛, 等. 基于MATLAB 6.X 的系统分析与设计-小波分析. 西安, 西安电子科技大学出版社, 2004, 153–188.

    Google Scholar 

  18. Hu Shigeng. Applications Of Functional Analysis. Beijing, Science Press, 2003, 289–326. 胡适耕. 应用泛函分析. 北京, 科学出版社, 2003, 289–326 (in Chinese).

    Google Scholar 

  19. Wang Deng, Miao Duoqian, and Wang Ruizhi. A new method of EEG classification with feature extraction based on wavelet packet decomposition. Acta Electronica Sinica, 41(2013)1, 193–198 (in Chinese). 王登, 苗夺谦, 王睿智. 一种新的基于小波包分解的 EEG 特征抽取与识别方法研究. 电子学报, 41(2013)1, 193–198.

    MathSciNet  Google Scholar 

  20. He Qing-bo. Vibration signal classification by wavelet packet energy flow manifold learning. Journal of Sound and Vibration, 332(2013)7, 1881–1894.

    Article  Google Scholar 

  21. Abul Kalam Mohammed Baki and Nemai C. Karmakar. Improved method of node and threshold selection in wavelet packet transform for UWB impulse radio signal denoising. Progress in Electromagnetics Research C, 38(2013)1, 241–257.

    Google Scholar 

  22. Saeid Niazmardi and Saeid Homayouni. An improved FCM algorithm based on the svdd for unsupervised hyperspectral data classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2013)2, 831–839.

    Article  Google Scholar 

  23. Hyun-Seung Son, Jin-Bae Park, and Young-Hoon Joo. External noise analysis algorithm based on FCM clustering. Transactions of the Korean Institute of Electrical Engineers, 60(2011)12, 2346–2351.

    Article  Google Scholar 

  24. Chen Taowei and Xin Ming. Feature extraction based on wavelet transform for radar emitter signals. Information and Electronic Engineering, 8(2010)4, 436–440 (in Chinese). 陈韬伟, 辛明. 基于小波变换的雷达辐射源信号特征提取. 信息与电子工程, 8(2010)4, 436–440.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Hefei, 230009, China

    Huadong Liang & Jianghong Han

  2. 38th Institute of China Electronics Technology Group Corporation, Hefei, 230088, China

    Huadong Liang

Authors
  1. Huadong Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianghong Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huadong Liang.

Additional information

Supported by the National Science and Technology Supported Program of China (No. 2011BAH24B05).

Communication author: Liang Huadong, born in 1982, male, Ph.D. candidate.

About this article

Cite this article

Liang, H., Han, J. Sorting radar signal based on wavelet characteristics of wigner-ville distribution. J. Electron.(China) 30, 454–462 (2013). https://doi.org/10.1007/s11767-013-3072-8

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11767-013-3072-8

Key words

CLC index

Search

Navigation