Skip to main content
SpringerLink
Log in

Performance improvement in multi-ship imaging for ScanSAR based on sparse representation

  • Research Paper
  • Special Issue
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

There is always a compromise between unambiguous wide-swath imaging and high cross-range resolution owing to the constraint of minimum antenna area for conventional single-channel spaceborne synthetic aperture radar (SAR) imaging. To overcome the inherent systemic limitation, multi-channel SAR imaging has been developed. Nevertheless, this still suffers from various problems such as high system complexity. To simplify the system structure, a novel algorithm for high resolution multi-ship ScanSAR imaging based on sparse representation is proposed in this paper, where the SAR imaging model is established via maximum a posterior estimation by utilizing the sparsity prior of multi-ship targets. In the scheme, a wide swath is generated in the ScanSAR mode by continuously switching the radar footprint between subswaths. Meanwhile, high cross-range resolution is realized from sparse subapertures by exploiting the sparsity feature of multi-ship imaging. In particular, the SAR observation operator is constructed approximately as the inverse of conventional SAR imaging and then high resolution SAR imaging including range cell migration compensation is achieved by solving the optimization. Compared with multi-channel SAR imaging, the system complexity is effectively reduced in the ScanSAR mode. In addition, enhancement of the cross-range resolution is realized by incorporating the sparsity prior with sparse subapertures. As a result, the amount of data is effectively reduced. Experiments based on measured data have been carried out to confirm the effectiveness and validity of the proposed algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Freeman A, Johnson W T K, Huneycutt B, et a1. The myth of the minimum SAR antenna area constraint. IEEE Trans Geosci Rem Sens, 2000, 38: 320–324

    Article  Google Scholar 

  2. Li Z F, Wang H X, Su T, et a1. Generation of Wide-Swath and High-Resolution SAR Images From Multichannel Small Spaceborne SAR Systems. IEEE Geosci Rem Sens Lett, 2005, 2: 82–86

    Article  Google Scholar 

  3. Gerhard K, Nicolas G, Alberto M. Multidimensional waveform encoding: A new digital beamforming technique for synthetic aperture radar remote sensing. IEEE Trans Geosci Rem Sens, 2008, 46: 31–46

    Article  Google Scholar 

  4. Nicolas G, Gerhard K, Alberto M. Multichannel azimuth processing in ScanSAR and TOPS mode operation. IEEE Trans Geosci Rem Sens, 2010, 48: 2994–3008

    Article  Google Scholar 

  5. Candès E, Romberg J, Tao T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory, 2006, 52: 489–509

    Article  MATH  Google Scholar 

  6. Kim Y Y, Nadar M, Bilgin A. Compressed sensing using a Gaussian scale mixtures model in wavelet domain. In: 17th IEEE International Conference on Image Processing (ICIP), 2010, 26. 3365–3368

  7. Cetin M, Kard W C. Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization. IEEE Trans Image Process, 2001, 10: 623–631

    Article  MATH  Google Scholar 

  8. Zhang L, Xing M D, Qiu C W, et al. Achieving higher resolution ISAR imaging with limited pluses via compressed sampling. IEEE Trans Geosci Rem Sens Lett, 2009, 6: 567–571

    Article  Google Scholar 

  9. Zhang L, Qiao Z J, Xing M D, et al. High Resolution ISAR imaging with sparse stepped-frequency waveforms. IEEE Trans Geosci Remote Sens, in press

  10. Liao M S, Wang C C, Wang Y, et al. Using SAR images to detect ships from sea clutter. IEEE Geosci Rem Sens Lett, 2008, 5: 194–198

    Article  Google Scholar 

  11. Montgomery D R, Pichel W, Calon C P. The use of satellite-based SAR in support of fisheries enforcement applications. In: IEEE International Geoscience and Remote Sensing Symposium Proceedings (IGARSS), 1998, vol. 3. 1388–1390

  12. Conte E, De Maio A. Exploiting persymmetry for CFAR detection in compound-Gaussian clutter. IEEE Trans on Aerosp Electron Syst, 2003, 39: 719–724

    Article  Google Scholar 

  13. Gini F, Greco M V, Verrazzani L. Detection problem in mixed clutter environment as a Gaussian problem by adaptive pre-processing. Electron Lett, 1995, 31: 1189–1190

    Article  Google Scholar 

  14. Samadi S, Cetin M, Masnadi-Shirazi M A. Sparse representation-based synthetic aperture radar imaging. IET Radar Sonar Navig, 2011, 5: 182–193

    Article  Google Scholar 

  15. Deledalle C A, Denis L, Tupin F. Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans Image Process, 2009, 18: 2661–2672

    Article  MathSciNet  Google Scholar 

  16. Wang H X, Liang Y, Xing M D, et a1. ISAR imaging via sparse frequency-stepped chirp signal. Sci China Inf Sci, 2012, 55: 877–888

    Article  Google Scholar 

  17. Ji S H, Xue Y, Carin L. Bayesian compressive sensing. IEEE Trans Signal Process, 2008, 56: 2346–2356

    Article  MathSciNet  Google Scholar 

  18. Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM Rev, 2001, 43: 129–159

    Article  MathSciNet  MATH  Google Scholar 

  19. Zhu C W. Stable recovery of sparse signals via regularized minimization. IEEE Trans Inform Theory, 2008, 54: 3364–3367

    Article  MathSciNet  Google Scholar 

  20. Liao M S, Wang C C, Wang Y, et al. Mitigation techniques for non-Gaussian sea clutter. IEEE Trans Geosci Rem Sens, 2008, 5: 194–198

    Article  Google Scholar 

  21. Figueiredo M A T, Nowak R D, Wright S J. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE J Sel Top Signal Process, 2007, 1: 586–597

    Article  Google Scholar 

  22. Deng B, Li X, Wang H Q, et al. Fast raw-signal simulation of extended scenes for missile-borne SAR with constant acceleration. IEEE Geosci Rem Sens Lett, 2011, 8: 44–48

    Article  Google Scholar 

  23. Franceschetti G, Guida R, Iodice A, et al. Efficient simulation of hybrid stripmap/spotlight SAR raw signals from extended scenes. IEEE Trans Geosci Rem Sens, 2004, 42: 2385–2396

    Article  Google Scholar 

  24. Prentiss N R. Depth of Field for SAR with Aircraft Acceleration. IEEE Trans Aerosp Electron Syst, 1984, 20: 603–616; 2008, 5: 194–198

    Article  Google Scholar 

  25. Alberto M, Josef M, Rolf S. Extended chirp scaling algorithm for air- and spaceborne SAR data processing in stripmap and ScanSAR imaging modes. IEEE Trans Geosci Rem Sens, 1996, 34: 1123–1136

    Article  Google Scholar 

  26. Zhang L, Xing M D, Qiu C W, et al. Resolution enhancement for inversed synthetic aperture radar imaging under low SNR via improved compressive sensing. IEEE Trans Geosci Rem Sens, 2010, 48: 3824–3838

    Article  Google Scholar 

  27. Moler C, Little J, Bangert S, et al. Pro-Matlab User’s Guide. Sherborn: The Math Works Inc., 1987

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, 710071, China

    Gang Xu, JiaLian Sheng, Lei Zhang & MengDao Xing

  2. National Key Laboratory of Microwave Imaging, Institute of Electronics, Chinese Academy of Sciences, Beijing, 100080, China

    MengDao Xing

Authors
  1. Gang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. JiaLian Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. MengDao Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gang Xu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xu, G., Sheng, J., Zhang, L. et al. Performance improvement in multi-ship imaging for ScanSAR based on sparse representation. Sci. China Inf. Sci. 55, 1860–1875 (2012). https://doi.org/10.1007/s11432-012-4626-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-012-4626-3

Keywords

Search

Navigation