SCIENCE CHINA Information Sciences, Volume 63 , Issue 2 : 129301(2020) https://doi.org/10.1007/s11432-018-9859-4

Extraction of a target in sea clutter via signal decomposition

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  • ReceivedDec 6, 2018
  • AcceptedApr 8, 2019
  • PublishedSep 24, 2019


There is no abstract available for this article.


Appendix A.


[1] Chen X, Guan J, Bao Z. Detection and Extraction of Target With Micromotion in Spiky Sea Clutter Via Short-Time Fractional Fourier Transform. IEEE Trans Geosci Remote Sens, 2014, 52: 1002-1018 CrossRef ADS Google Scholar

[2] Ward K D, Tough R J A, Watts S. Sea clutter: Scattering, the K distribution and radar performance. Waves Random Complex Media, 2007, 17: 233-234 CrossRef Google Scholar

[3] Sira S P, Cochran D, Papandreou-Suppappola A, et al. A subspace-based approach to sea clutter suppression for improved target detection. In: Proceedings of the 40th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, 2006. 752--756. Google Scholar

[4] Shi Y L, Shui P L. Target detection in high-resolution sea clutter via block-adaptive clutter suppression. IET Radar Sonar Navig, 2011, 5: 48-57 CrossRef Google Scholar

[5] Carretero-Moya J, Gismero-Menoyo J, Asensio-Lopez A. Small-Target Detection in High-Resolution Heterogeneous Sea-Clutter: An Empirical Analysis. IEEE Trans Aerosp Electron Syst, 2011, 47: 1880-1898 CrossRef ADS Google Scholar

[6] Farshchian M. Target Extraction and Imaging of Maritime Targets in the Sea Clutter Spectrum Using Sparse Separation. IEEE Geosci Remote Sens Lett, 2017, 14: 232-236 CrossRef ADS Google Scholar

[7] Selesnick I W. Sparse signal representations using the tunable Q-factor wavelet transform. Proc SPIE, 2011, 8138: 815--822. Google Scholar

  • Figure 1

    (Color online) (a) Target at the edge of sea clutter; (b) target covered by sea clutter; (c) probability of detection versus different SCR levels.


    Algorithm 1 Signal separation algorithm


    initialization ${w_1}$, ${w_2}$, ${d_1}$, ${d_2}$, $\lambda~$, $\mu$, $N$;


    for $i=1$ to $N$

    ding192 Computing sparse coefficient ${u_1},{u_2}$:${u_1}~=~{\rm~soft}({w_1}~+~{d_1},0.5{\lambda~_1}/\mu~)~-~{d_1}$, ${u_2}~=~{\rm~soft}({w_2}~+~{d_2},0.5{\lambda~_2}/\mu~)~-~{d_2}$;

    ding193 Refactoring $s,c$:$s=~{\rm~FrFT}_{\rm{-~opt}}(~{{u_1}})$,$c=~{\rm~ISTFT}(~{{u_2}}~)$;

    ding194 Calculating residual $R$: $R~=~x~-~s~-~c$;

    ding195 Calculating residual coefficient ${d_1},{d_2}$:${d_1}~=~\frac{1}{2}~{\rm~FrFT}_{~\rm{opt}}(~{{R}})$,${d_2}~=~\frac{1}{2}~{\rm~STFT}(~{{R}}~)$;

    ding196 Updating the sparse coefficient ${w_1},{w_2}$:${w_1}~=~{d_1}~+~{u_1}$,${w_2}~=~{d_2}~+~{u_2}$;

    end for

    Output: $s=~{\rm~FrFT}_{\rm{-~opt}~}(~{{w_1}})$,