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

  • Qi-fang HeEmail author
  • 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)


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.


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 


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Copyright information

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

Authors and Affiliations

  • Qi-fang He
    • 1
    Email author
  • 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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