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Russian Journal of Nondestructive Testing

, Volume 54, Issue 1, pp 44–54 | Cite as

Instantaneous Frequency Estimation for Motion Echo Signal of Projectile in Bore Based on Polynomial Chirplet Transform

  • Jian Wang
  • Yan Han
  • Li Ming Wang
  • Pi Zhuang Zhang
  • Ping Chen
Acoustic Methods
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Abstract

Microwave interferometer is one of the devices for measuring the movement travel–varying or time-varying velocity of projectile in bore. Microwave interferometer first obtains the Doppler echo signal including the motion information of the projectile in bore, then the velocity is measured based on instantaneous frequency estimation (IFE) of the processed and transformed signal. The parametric time-frequency analysis method can make spectral energy of nonlinear frequency modulation (FM) signal concentrate at some range in the new transform domain. As the motion echo signal of projectile in bore (MSPB) is a nonlinear FM signal, it could be described by polynomial chirplet, one of polynomial FM signal modes, which is used to construct transform kernel for the signal. In this paper, Polynomial chirplet transform (PCT) method is proposed to analyze the simulation and experiment echo signals of projectile in bore. The estimation error and Renyi entropy are used to measure quantify of the time-frequency distribution. Compared with short-time Fourier transform (STFT) method and Wigner-Ville distribution (WVD) method, our results show that the PCT method has most powerful anti-interference performance and highest accuracy of instantaneous frequency estimation for the simulation signal, and lowest Renyi entropy of the instantaneous frequency estimation for the experiment signal. In general, the PCT method has powerful anti-interference performance and high time-frequency concentration and accuracy of instantaneous frequency estimation for the motion echo signal of projectile in bore.

Keywords

time-frequency concentration instantaneous frequency estimation polynomial chirplet transform parametric time-frequency analysis 
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References

  1. 1.
    Wang, L.M., Zhao, X., Liu, H.T., et al., Method of test data processing on microwave interferometer, Gun Launch Control J., 2004, no. 04, pp. 63–66,70.Google Scholar
  2. 2.
    Zhao, L.Q., The Application of Time-Frequency Analysis in the Interior Ballistic Velocity Measurement Radar, Nanjing: Nanjing University of Science and Technology, 2006.Google Scholar
  3. 3.
    Cai, D.Q., The Fractional Fourier Transform and Its Applications in Microwave Interferometer, Xi’an: Xi’an Electronic and Science University, 2010.Google Scholar
  4. 4.
    Wei, Y.Y. and Yao, J.J., Velocity measurement of moving target based on wavelet denoising and improved extremum method, Micro Comput. Inform., 2010, no. 19, pp. 217–218,200.Google Scholar
  5. 5.
    Liu, D., Zhang, P.Z., and Ma, C.Y., A precise method to estimate location of Doppler signal’s extreme value points, Laser Optoelectronics Progress, 2015, no. 07, pp. 53–57.Google Scholar
  6. 6.
    Chen, Z.X. and Shi, L.L., Data processing system of microwave interferometer based on LabVIEW, Sci. Technol. Inf., 2007, no. 24, p.6.Google Scholar
  7. 7.
    Liu, B., Xiao, J., Guo, Y.L., et al., Study on testing methods of Ballet’s parameters in Great Calibre Artillery, J. Projectiles, Rockets, Missiles, Guidance, 2010, vol. 30, no. 01, pp. 167–169,172.Google Scholar
  8. 8.
    Kong, W., Wang, B.J., Zhang, Z.Y., et al., The processing method of the interior ballistic Doppler signal, J. Projectiles, Rockets, Missiles, Guidance, 2013, vol. 33, no. 03, pp. 143–145.Google Scholar
  9. 9.
    Kong, W., Xiao, J., Chang, Z.T., et al., Processing of projectile motion signal in bore based on phase unwrapping, J. Projectiles, Rockets, Missiles, Guidance, 2012, vol. 32, no. 2, pp. 189–192.Google Scholar
  10. 10.
    Liu, D., Zhang, P.Z., and Ma, C.Y., Estimates of radar echo signal and spectrum analysis based on the least square method and STFT, Electron. Des. Eng., 2015, vol. 23, no. 17, pp. 131–134.Google Scholar
  11. 11.
    Yao, J.J. and Han, Y., Application of time-frequency analysis to velocity measurement of varying accelerated moving target, Fire Control, Command Control, 2011, vol. 36, no. 05, pp. 133–135,139.Google Scholar
  12. 12.
    Yao, J.J., Wang, L.M., and Han, Y., Signal Processing of Millimeter Wave Velocity Measurement Radar Based on Wigner-Ville Distribution, Chinese Institute of Electronics, 2009.Google Scholar
  13. 13.
    Dong, Y.H. and Cui, Y.Q., Analysis of a new joint time-frequency distribution of suppressing cross-term, Res. J. Appl. Sci., Eng. Technol., 2012, vol. 4, no. 11, pp. 1580–1584.Google Scholar
  14. 14.
    Sucic1, V., Saulig, N., and Boashash, B., Estimating the number of components of a multicomponent nonstationary signal using the short-term time-frequency Renyi entropy, EURASIP J. Adv. Signal Process., 2011, p.125.Google Scholar
  15. 15.
    Chen, X. and Xu, J.Z., Application of Wigner higher-order spectra in analysis of projectile motion in bore, J. Nanjing Univ. Sci. Technol., 2012, vol. 36, no. 03, pp. 442–447.Google Scholar
  16. 16.
    Yang, J., Chen, X., and Li, X.L., The processing of the echo signal of the projectile in bore based on the polynomial Wigner-Ville distribution, Sichuan Acta Armamentarii, 2012, no. 08, pp. 112–114,123.Google Scholar
  17. 17.
    Wang, D.W., Fan, J.L., and Li, Y.H., Instantaneous frequency estimation algorithm of non-linear frequency modulation signal in low SNR based on generalized S-transform, Int. J. Digital Content Technol., Its Appl., 2012, vol. 6, no. 17, p.627.CrossRefGoogle Scholar
  18. 18.
    Jiang, L., Li, L., Zhao, G., et al., Instantaneous frequency estimation of nonlinear frequency-modulated signals under strong noise environment, Circuits, Systems, Signal Process., 2016, pp. 1–11.Google Scholar
  19. 19.
    Yang, Y., Peng, Z.K., Dong, X.J., et al., General parameterized time-frequency transform, IEEE Trans. Signal. Process., 2014, vol. 62, no. 11, pp. 2751–2764.CrossRefGoogle Scholar
  20. 20.
    Lu, W.L., Xie, J.W., Wang, H.M., et al., Signal component extraction method based on polynomial chirp Fourier transform, Acta Phys. Sin., 2016, vol. 65, no. 08, pp. 9–18.Google Scholar
  21. 21.
    Peng, Z.K., Meng, G., Chu, F.L., et al., Polynomial chirplet transform with application to instantaneous frequency estimation, IEEE Trans. Instrum. Measurement, 2011, vol. 60, no. 09, pp. 3222–3229.CrossRefGoogle Scholar
  22. 22.
    Yang, Y., Zhang, W.M., Peng, Z.K., et al., Multicomponent signal analysis based on polynomial chirplet transform, IEEE Trans. Ind. Electron., 2013, vol. 60, no. 9, pp. 3948–3956.CrossRefGoogle Scholar
  23. 23.
    Xu, L.J., Yang, Y.X., and Yu, S.D., Analysis of moving source characteristics using polynomial chirplet transform, J. Acoust. Soc. Am., 2015, vol. 137, no.4.Google Scholar
  24. 24.
    Zhang, X.B., Interior Ballistics of Guns, Beijing: Beijing Institute of Technology Press, 2014.Google Scholar
  25. 25.
    Boualem Boashash, Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, Academic, 2015.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  • Jian Wang
    • 1
    • 2
  • Yan Han
    • 1
    • 2
  • Li Ming Wang
    • 1
    • 2
  • Pi Zhuang Zhang
    • 1
    • 2
  • Ping Chen
    • 1
    • 2
  1. 1.Key Laboratory of Information Detection and ProcessingNorth University of ChinaTaiyuan ShanxiChina
  2. 2.School of Information and Communication EngineeringNorth University of ChinaTaiyuan, ShanxiChina

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