Journal of Communications and Networks

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Method for Feature Extraction of Radar Full Pulses Based on EMD and Chaos Detection

  • Guo, Qiang (College of Information and Communication Engineering, Harbin Engineering University) ;
  • Nan, Pulong (College of Information and Communication Engineering, Harbin Engineering University)
  • Received : 2013.02.25
  • Accepted : 2013.12.11
  • Published : 2014.02.28
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Abstract

A novel method for extracting frequency slippage signal from radar full pulse sequence is presented. For the radar full pulse sequence received by radar interception receiver, radio frequency (RF) and time of arrival (TOA) of all pulses constitute a two-dimensional information sequence. In a complex and intensive electromagnetic environment, the TOA of pulses is distributed unevenly, randomly, and in a nonstationary manner, preventing existing methods from directly analyzing such time series and effectively extracting certain signal features. This work applies Gaussian noise insertion and structure function to the TOA-RF information sequence respectively such that the equalization of time intervals and correlation processing are accomplished. The components with different frequencies in structure function series are separated using empirical mode decomposition. Additionally, a chaos detection model based on the Duffing equation is introduced to determine the useful component and extract the changing features of RF. Experimental results indicate that the proposed methodology can successfully extract the slippage signal effectively in the case that multiple radar pulse sequences overlap.

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References

  1. D. G. Wilkinson and A. W. Watson, "Use of Metric Techniques in ESM Data Processing," Proc. of IEE Radar and Signal Processing: Part F, vol. 132, no. 7, pp. 121-125, 1985.
  2. D. J. Milojevic and B. M. Popovic, "Improved Algorithm for the Deinterleaving of Radar Pulses," Proc. of IEE Radar and Signal Processing: Part F, vol. 139, no. 1, pp. 98-104, 1992. https://doi.org/10.1049/ip-f-2.1992.0012
  3. S. G. Mallat and Z. Zhang, "Matching Pursuit with Time-frequency Dictionaries," IEEE Trans. Signal Process., vol. 41, no. 12, pp. 3397-3415, 1993. https://doi.org/10.1109/78.258082
  4. S. S. Chen, D. L. Donoho, and M. A. Saunder, "Atomic Decomposition by Basis Pursuit," SIAM J. Sci. Comput., vol. 20, no. 1, pp. 33-61, 1998. https://doi.org/10.1137/S1064827596304010
  5. X. Q.Wang et al., "AMethod for Radar Emitter Signal Recognition Based on Time-frequency Atom Features," J. Infrared and Millimeter Waves, vol. 30, no. 6, pp. 566-570, 2011.
  6. M. Zhu, W. D. Jin, and L. Z. Hu, "Cascade Feature Extraction for Radar Emitter Signals Based on Atomic Decomposition," J. Southwest Jiaotong University, vol. 42, no. 6, pp. 659-664, 2007.
  7. Y. W. Pu et al., "Extracting the Main Ridge Slice Characteristics of Fuzzy Function for Radar Emitter Signal," J. Infrared and Millimeter Waves, vol. 27, no. 2, pp. 133-137, 2008. https://doi.org/10.3724/SP.J.1010.2008.00133
  8. Y. Shi, Y. W. Pu, and T. F. Zhang, "Intelligently Searching the Slice of Ambiguity Function Main Ridge Based on Superiority Inheritance," J. Infrared and Millimeter Waves, vol. 32, no. 1, pp. 80-85, 2013. https://doi.org/10.3724/SP.J.1010.2013.00080
  9. Z. T. Huang, Y. Y. Jiang, and W. L.Jiang, "The Automatic Analysis of Intrapulse Modulation Characteristics Based on the Relatively Nonambiguity Phase Restoral," J. China Institute of Communications, vol. 24, no. 4, pp. 153-160, 2003.
  10. Y. M. Wei, Z. T. Huang, and F. H. Wang, "The Analysis of PSK Modulation Rules Based on the Coherent Integration of Single Pulse," Signal Processing, vol. 22, no. 2, pp. 281-284, 2006.
  11. G. X. Zhang, H. N. Rong, and W. D. Jin, "Application of Support Vector Machine to Radar Emitter Signal Recognition," J. Southwest Jiaotong University, vol. 41, no. 1, pp. 25-30, 2006.
  12. Q. Guo, C. H. Wang, and Z. Li, "Support Vector Clustering and Type- Entropy Based Radar Signal Sorting Method," J. Xi'an Jiaotong University, vol. 44, no. 8, pp. 63-67, 2010.
  13. S. Q. Wang et al., "Multi-parameter Radar Signal Sorting Method Based on Fast Support Vector Clustering and Similitude Entropy," J. Electronics & Information Technology, vol. 33, no. 11, pp. 2735-2741, 2011. https://doi.org/10.3724/SP.J.1146.2011.00261
  14. T.W. Chen,W. D. Jin, and J. Li, "The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis Feature Extraction of Radar Emitter Signals Based on Autocorrelation Function of First Difference," Computer Engineering and Applications, vol. 47, no. 26, pp. 143-145, 2001.
  15. K. Falconer, Fractal Geometry: Mathematical Foundations and Applications. Chichester: John Wiley and Sons, 1990.
  16. H. Zhu and S. R. Ge, "Comparison of Fractal Characterization Effects of Structure Function and Mean Square Root," J. China University of Mining & Technology, vol. 33, no. 4, pp. 396-399, 2004.
  17. C. G. Li, J. C. Tian, and Y. Liao, "Structure Function and Its Application for Fractal Surface," Aviation Metrology & Measurement Technology, vol. 17, no. 4, pp. 6-8, 1997.
  18. N. E. Huang et al., "The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis," Proc. R. Soc. A, vol. 454, no. 1971, pp. 903-995, 1998. https://doi.org/10.1098/rspa.1998.0193
  19. B. J. Yang and Y. Li, Chaotic Oscillator Detection Introduction. Publishing House of Electronics Industry, 2004.
  20. J. G. Wang et al., "Chaos-Based Weak Signal Detection Approach via Duffing Oscillator," Chinese J. Electron Devices, vol. 30, no. 4, pp. 1380- 1383, 2007.