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Parametric Sparse Recovery and SFMFT Based M-D Parameter Estimation with the Translational Component

  • Qi-fang He
  • Han-yang Xu
  • Qun Zhang
  • Yi-jun Chen
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 227)

Abstract

The micro-Doppler effect (m-D effect) provides unique signatures for target discrimination and recognition. In this paper, we consider a solution to the m-D parameter estimation. This method mainly consists of two procedures, with the first being the radar returns decomposition to extract the m-D components in Bessel domain. Then the parameter estimation issue is transformed as a parametric sparse recovery solution. A parametric sparse dictionary, which depends on m-D frequencies, is constructed according to the inherent property of the m-D returns. Considering that the m-D frequency is unknown, the discretizing m-D frequency range for the parametric dictionary matrix is calculated by the sinusoidal frequency modulated Fourier transform (SFMFT). In this manner, the finer m-D frequency, initial phases, maximum Doppler amplitudes and scattering coefficients are obtained by solving the sparse solution of the m-D returns. The simulation results verify the effectiveness.

Keywords

Compressive Sensing (CS) micro-Doppler effect (m-D effect) Parametric sparse representation Sinusoidal Frequency Modulated Fourier Transform (SFMFT) K-resolution Fourier-Bessel (k-FB) series Parameter estimation 

References

  1. 1.
    Wang, D.C.: An overview of micro-doppler radar. J. CAEIT 7(6), 575–580 (2012). (in Chinese)Google Scholar
  2. 2.
    Ruegg, M., Meier, E., Nuesch, D.: Vibration and rotation in millimeter-wave SAR. IEEE Trans. Geosci. Remote Sens. 45(2), 293–304 (2007)CrossRefGoogle Scholar
  3. 3.
    Liu, Z., Wei, X.Z., Li, X.: Aliasing-free micro-doppler analysis based on short-time compressed sensing. IET Sig. Process. 8(2), 176–187 (2014)CrossRefGoogle Scholar
  4. 4.
    Suresh, P., Thayaparan, T., Obulesu, T., Venkataramaniah, K.: Extracting micro-doppler radar signatures from rotating targets using fourier-bessel transform and time-frequency analysis. IEEE Trans. Geosci. Remote Sens. 52(6), 3204–3210 (2014)CrossRefGoogle Scholar
  5. 5.
    He, Q.F., Wang, J.D., Wang, K., Wu, Y.G., Zhang, Q.: Multi-component LFM signals detection and separation using fourier-bessel series expansion. In: RADAR 2016 Conference, Guangzhou, pp. 1749–1753. IEEE Press (2016)Google Scholar
  6. 6.
    Peng, B., Wei, X.Z., Deng, B., Chen, H.W., Liu, Z., Li, X.: A sinusoidal frequency modulation fourier transform for radar-based vehicle vibration estimation. IEEE Trans. Instrument. Measure. 63(9), 2188–2199 (2014)CrossRefGoogle Scholar
  7. 7.
    Luo, Y., Zhang, Q., Wang, G.Z., Guan, H., Bai, Y.Q.: Micro-motion signature extraction method for wideband radar based on complex image OMP decomposition. J. Radars. 1(4), 361–369 (2012). (in Chinese)CrossRefGoogle Scholar
  8. 8.
    Orovic, I., Stankovic, S., Thayaparan, T.: Time-frequency-based instantaneous frequency estimation of sparse signals from incomplete set of samples. IET Sig. Process. 8(3), 239–245 (2014)CrossRefGoogle Scholar
  9. 9.
    Stankovic, L., Orovic, I., Stankovic, S., Amin, M.: Compressive sensing based separation of nonstationary and stationary signals overlapping in time-frequency. IEEE Trans. Sig. Process. 61(18), 4562–4572 (2013)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Flandrin, P., Borgnat, P.: Time-frequency energy distributions meet compressed sensing. IEEE Trans. Sig. Process. 58(6), 2974–2982 (2010)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Chen, X.L., Dong, Y.L., Huang, Y., Guan J.: Detection of marine target with quadratic modulated frequency micromotion signature via morphological component analysis. In: 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, Pisa, Italy, pp. 220–224 (2015)Google Scholar
  12. 12.
    Stankovic, S., Orovic, I., Pejakovic, T., Orovic, M.: Compressive sensing reconstruction of signals with sinusoidal phase modulation: application to radar micro-doppler. In: 22nd Telecommunications forum TELFOR, Belgrade, pp. 565–568 (2014)Google Scholar
  13. 13.
    Orovic, I., Stankovic, S., Amin, M.: Compressive sensing for sparse time-frequency representation of nonstationary signals in the presence of impulsive noise. In: Proceedings of SPIE, vol. 8717, United States (2013)Google Scholar
  14. 14.
    Li, G., Varshney, P.K.: Micro-doppler parameter estimation via parametric sparse representation and pruned orthogonal matching pursuit. IEEE J. Sel. Topic. Appl. Earth Obs. Remote Sens. 7(12), 4937–4948 (2014)CrossRefGoogle Scholar
  15. 15.
    Xia, P., Wan, X.R., Yi, J.X.: Micromotion parameters estimation for rotating structures on target in passive radar. Chin. J Radio Sci. 31(4), 676–682 (2016)Google Scholar
  16. 16.
    Hasar, U.C., Barroso, J.J., Sabah, C., Kaya, Y.: Resolving phase ambiguity in the inverse problem of reflection-only measurement methods. Progr. Electromagn. Res. 129, 405–420 (2012)CrossRefGoogle Scholar
  17. 17.
    Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Qi-fang He
    • 1
  • Han-yang Xu
    • 2
  • Qun Zhang
    • 1
  • Yi-jun Chen
    • 1
  1. 1.Information and Navigation CollegeAir Force Engineering UniversityXi’anChina
  2. 2.School of Electronic EngineeringXidian UniversityXi’anChina

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