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Target decomposition and recognition from wide-angle SAR imaging based on a Gaussian amplitude-phase model

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Abstract

Wide-angle synthetic aperture radar (W-SAR) imaging accounts for multi-azimuthal scattering and is feasible for retrieving more comprehensive features of complex targets. Because a typical target is seen as composed of its components (typically, some simple geometric objects), a Gaussian amplitude-phase (GAP) model has been developed for the analysis of multi-azimuthal scattering from these objects. Based on the time-frequency analysis of wide-angle scattering, the parameters of the GAP model were estimated, including the Gaussian variance, the surface curvature, and the number of objects in all imaged pixels. Numerical simulations and real measurements demonstrate the capability of the GAP model for decomposing and recognizing complex electric-large targets.

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References

  1. 1

    Moses R L, Potter L C, Çetin M. Wide-angle SAR imaging. In: Proceedings of SPIE in Algorithms for Synthetic Aperture Radar Imagery XI, Orlando, 2004. 5427: 164–175

  2. 2

    Lanterman A, Munson D, Wu Y. Wide-angle radar imaging using time-frequency distributions. IEE Proc-Radar Sonar Navig, 2003, 150: 203–211

  3. 3

    Ash J N, Ertin E, Potter L C, et al. Wide-angle synthetic aperture radar imaging: models and algorithms for anisotropic scattering. IEEE Signal Process Mag, 2014, 31: 16–26

  4. 4

    Ferro-Famil L, Reigber A, Pottier E, et al. Scene characterization using subaperture polarimetric SAR data. IEEE Trans Geosci Remote Sens, 2003, 41: 2264–2276

  5. 5

    Duquenoy M, Ovarlez J-P, Ferro-Famil L, et al. Scatterers characterisation in radar imaging using joint time-frequency analysis and polarimetric coherent decompositions. IET Radar Sonar Navig, 2010, 4: 384–402

  6. 6

    Duquenoy M, Ovarlez J-P, Ferro-Famil L, et al. Characterization of scatterers by their energetic dispersive and anisotropic behaviors in high-resolution laboratory radar imagery. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec, 2014. 4715–4718

  7. 7

    Spigai M, Tison C, Souyris J-C. Time-frequency analysis in high-resolution SAR imagery. IEEE Trans Geosci Remote Sens, 2011, 49: 2699–2711

  8. 8

    Dungan K E, Potter L C. Classifying vehicles in wide-angle radar using pyramid match hashing. IEEE J Sel Top Signal Process, 2011, 5: 577–591

  9. 9

    Fuller D F, Saville M A. A high-frequency multipeak model for wide-angle SAR imagery. IEEE Trans Geosci Remote Sens, 2013, 51: 4279–4291

  10. 10

    Zhao Y, Lin Y, Hong W, et al. Adaptive imaging of anisotropic target based on circular-SAR. Electron Lett, 2016, 52: 1406–1408

  11. 11

    Jackson J A, Rigling B D, Moses R L. Canonical scattering feature models for 3D and bistatic SAR. IEEE Trans Aerosp Electron Syst, 2010, 46: 525–541

  12. 12

    Rihaczek A W, Hershkowitz S J. Radar Resolution and Complex-Image Analysis. Norwood: Artech House, Inc., 1996

  13. 13

    Jackson J A, Moses R L. Synthetic aperture radar 3D feature extraction for arbitrary flight paths. IEEE Trans Aerosp Electron Syst, 2012, 48: 2065–2084

  14. 14

    Trintinalia L C, Bhalla R, Ling H. Scattering centre parameterization of wide-angle backscattered data using adaptive Gaussian representation. IEEE Trans Antennas Propag, 1997, 45: 1664–1668

  15. 15

    Li J, Ling H. Application of adaptive chirplet representation for ISAR feature extraction from targets with rotating parts. IEE Proc-Radar Sonar Navig, 2003, 150: 284–291

  16. 16

    Leducq P, Ferro-Famil L, Pottier E. Matching-pursuit-based analysis of moving objects in polarimetric SAR images. IEEE Geosci Remote Sens Lett, 2008, 5: 123–127

  17. 17

    Qian S E, Chen D P. Joint time-frequency analysis. IEEE Signal Process Mag, 1999, 16: 52–67

  18. 18

    Çetin M, Ivana S, Onhon N O, et al. Sparsity-driven synthetic aperture radar imaging: reconstruction, autofocusing, moving targets, and compressed sensing. IEEE Signal Process Mag, 2014, 31: 27–40

  19. 19

    Davis G, Mallat S, Avellaneda M. Adaptive greedy approximations. Constr Approx, 1997, 13: 57–98

  20. 20

    Potter L C, Ertin E, Parker J T, et al. Sparsity and compressed sensing in radar imaging. Proc IEEE, 2010, 98: 1006–1020

  21. 21

    Naidu K, Lin L. Data Dome: full k-space sampling data for high-frequency radar research. In: Proceedings of SPIE in Algorithms for Synthetic Aperture Radar Imagery XI, Orlando, 2004. 5427: 200–207

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61471127) and Shanghai Yangpu Ding-Yuan Foundation. Y. C. Li is grateful to Dr. A.S. Khwaja for useful discussion on GAP.

Author information

Correspondence to Ya-Qiu Jin.

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Conflict of interest The authors declare that they have no conflict of interest.

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Li, Y., Jin, Y. Target decomposition and recognition from wide-angle SAR imaging based on a Gaussian amplitude-phase model. Sci. China Inf. Sci. 60, 062305 (2017). https://doi.org/10.1007/s11432-016-0572-3

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Keywords

  • wide-angle SAR
  • Gaussian amplitude-phase
  • time-frequency
  • target decomposition
  • target recognition
  • 062305