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.


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[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

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


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