Precession-Identification Based on Sparse Representation

  • Yi Xu
  • Peng You
  • Hongqiang Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 238)


The precession-identification, on the background of ballistic missile defense, is studied. Based on sparse representation, a detecting procedure and a corresponding parameter estimation principle are proposed in this paper. The method can judge the existence of precession-modulated signals and estimate the precession parameters. The experimental results demonstrate the effectiveness of the method. The precession-identification method would be useful for the practical application.


Detection Probability Sparse Representation Projection Location Sparse Solution Sparsity Level 
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  1. 1.
    Chen VC, Li F, Ho S et al (2006) Micro-Doppler effect in radar: phenomenon, model, and simulation study. IEEE Trans Aerosp Electron Syst 42:2–21CrossRefGoogle Scholar
  2. 2.
    Cai C, Liu W, Fu JS et al (2010) Radar micro-doppler signature analysis with HHT. IEEE Trans Aerosp Electron Syst 46:929–938CrossRefGoogle Scholar
  3. 3.
    Thayaparan T, Suresh K, Qian S et al (2010) Micro-doppler analysis of a rotating target in SAR. IET Signal Process 4:245–255CrossRefGoogle Scholar
  4. 4.
    Li K (2010) Research on feature extraction and parameters estimation for radar targets with micro-motions. PhD dissertation, National University of Defense Technology, Changsha, pp 19–22Google Scholar
  5. 5.
    Andreas K (1996) An introduction to the mathematical theory of inverse problems. Applied mathematical sciences. Springer, New York, 120pGoogle Scholar
  6. 6.
    Cetin M, Karl WC (2001) Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization. IEEE Trans Image Process 10:623–631MATHCrossRefGoogle Scholar
  7. 7.
    Wang Z, Zhu J (2001) Super-resolution techniques in synthetic aperture radar image, 1st edn. Science, Beijing, pp 131–133Google Scholar
  8. 8.
    Ausin CD, Moses RL, Ash JN et al (2010) On the relation between sparse reconstruction and parameter estimation with model order selection. IEEE J Sel Top Signal Process 4:560–569CrossRefGoogle Scholar
  9. 9.
    Huo K (2011) Research on feature extraction for target with micro-motion based on new OFDM radar signals. PhD dissertation, National University of Defense Technology, Changsha, China, pp 71–75Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Electronic Science and EngineeringNational University of Defense TechnologyChangshaPeople’s Republic of China

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