Scattering-Based Tomography for HRR and SAR Prediction

  • B. S. Denney
  • R. J. P. De Figueiredo
Chapter

Abstract

This paper introduces a method for predicting HRR radar signatures and SAR images by creating a parametric three-dimensional scattering model from existing measured or model-based HRR signatures and/or SAR images. The method identifies potential three-dimensional persistent scatterers and estimates their scattering patterns. The results are parametric HRR signature and SAR image reconstruction functions of range, azimuth, and elevation.

The modeling is accomplished through a scattering-based tomography technique. This technique localizes potential scatterers by using a filtered back-projection algorithm for the inverse radon transform. Once found, potential scatterers may then have their two-dimensional (azimuth and elevation) scattering patterns parameterized through the use of a truncated spherical harmonic series.

Results using the reconstructions from HRR data are presented. A M109 model is reconstructed based on HRR signatures. The model allows us to predict what the vehicle would look like from any arbitrary orientation using SAR. Finally an M548 vehicle is modeled using 26 measured HRR signatures. The model is shown to be better than the synthetic model data. Additionally we show that the new model results can be combined with the synthetic data to provide a better target model for signature matching.

Key Words

ATR HRR SAR tomography radar prediction 

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References

  1. 1.
    J.D. Leonard, B.S. Denney, G.C. Lai, R.J.P. de Figueiredo, and A.R. Nolan, “Combining Measured Radar Signatures with Computer Generated Signatures for Aircraft Identification,” in Sixth Automatic Target Recognizer Systems and Technology Conference, 1997.Google Scholar
  2. 2.
    A.R. Nolan and J.A. Hughes, “Synthetic Signature Analysis Using Ray Trace Decomposition,” in SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery VI, April 1999.Google Scholar
  3. 3.
    B.S. Denney, S.N. Richman, and R.J.R de Figueiredo, “Model Based HRR Radar ATR: Combining Synthetic Models with Limited Measured Observations,” in SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery VI, April 1999, pp. 437-447.Google Scholar
  4. 4.
    B.S. Denney, R.J.R de Figueiredo, and R. Williams, “A New Approach to Parametric HRR Signature Modeling for Improved 1-D ATR,” in SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery VII; Proceedings of SPIE, April 2000, pp. 372-383.Google Scholar
  5. 5.
    D.C. Munson, J.D. O’Brien, and W.K. Jenkins, “A Tomographic Formulation of Spotlight-Mode Synthetic Aperture Radar,” Proceedings of the IEEE, vol. 71, no. 8, August 1983, pp. 917–925.CrossRefGoogle Scholar
  6. 6.
    Y. Dia, E.J. Rothwell, K.M. Chen, and D.R Nyquist, “Time-Domain Imaging of Radar Targets Using Algorithms for Reconstruction from Projections,” IEEE Transactions on Antennas and Propagation, vol. 45, no. 8, August 1997, pp. 1227–1235.CrossRefGoogle Scholar
  7. 7.
    M.R. Allen, J.M. Jauregui, and L.E. Hoff, “Fopen-Sar Detection by Direct Use of Simple Scattering Physics,” Proc. SPIE, Algorithms for Synthetic Aperture Radar Imagery II, vol. 2487, 1995, pp. 45–55.CrossRefGoogle Scholar
  8. 8.
    L.C. Trintinalia, R. Bhalla, and H. Ling, “Scattering Center Parameterization of Wide-Angle Backscattered Data Using Adaptive Gaussian Representation,” IEEE Transactions on Antennas and Propagation, vol. 45, no. 11, November 1997, pp. 1664–1668.CrossRefGoogle Scholar
  9. 9.
    M.J. Gerry, L.C. Potter, I.J. Gupta, and A. van der Merwe, “A Parameteric Model for Synthetix Aperture Radar Measurements,” IEEE Transactions on Antennas and Propagation, vol. 47, no. 7, July 1999, pp. 1179–1188.CrossRefGoogle Scholar
  10. 10.
    Y. Dia, E.J. Rothwell, K.M. Chen, and D.R Nyquist, “Time-Domain Imaging of Radar Targets Using Sinogram Restoration for Limited-View Reconstruction,” IEEE Transactions on Antennas and Propagation, vol. 47, no. 8, August 1999, pp. 1323–1329.CrossRefGoogle Scholar
  11. 11.
    B.S. Denney, “Rotation Clustering for Robot Vision,” PhD thesis, University of California, Irvine, Irvine, California, 1996.Google Scholar
  12. 12.
    M. Skolnik (Ed.), Radar Handbook, New York: McGraw-Hill, 1990.Google Scholar
  13. 13.
    W. Smith, T. Irons, J. Riordan, and S. Sayre, “Peak Stability Defived from Phase History in Synthetic Aperture Radar,” Proc. SPIE, Algorithms for Synthetic Aperture Radar Imagery VI, vol. 3721, 1999, pp. 450–461.CrossRefGoogle Scholar
  14. 14.
    A. Rosenfeld and A.C. Kak, Digital Picture Processing, volume 1, 2nd ed., New York: Academic Press, 1982.Google Scholar
  15. 15.
    Z.H. Cho, J.P. Jones, and M. Singh, Foundations of Medical Imaging, New York: Wiley-Interscience, 1993.Google Scholar
  16. 16.
    J. Wissinger, R. Ristroph, J. Diemunsch, W. Severson, and E. Freudenthal, “Mstar’s Extensible Search Engine and Model-Based Inferencing Toolkit,” SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery VI, April 1999, pp. 554-570.Google Scholar
  17. 17.
    R. Gonzalez and R. Woods, Digital Image Processing, New York: Addison-Wesley, 1992.Google Scholar
  18. 18.
    K. Segalovitz and B.R. Frieden, “A “Clean”-Type Deconvolution Algorithm,” Astron. Astrophys., vol. 70, 1978, pp. 335–343.Google Scholar
  19. 19.
    R. Bhalla and H. Ling, “Three-Dimensional Scattering Center Extraction Using the Shooting and Bouncing Ray Technique,” IEEE Transactions on Antennas and Propagation, vol. 44, no. 11, 1996, pp. 1445–1453.CrossRefGoogle Scholar
  20. 20.
    R. Bhalla, J. Moore, and H. Ling, “A Global Scattering Center Representation of Complex Targets Using the Shooting and Bouncing Ray Technique,” IEEE Transactions on Antennas and Propagation, vol. 45, no. 12, 1997, pp. 1850–1854.CrossRefGoogle Scholar
  21. 21.
    P. Dennery and A. Krzywicki, Mathematics for Physicists, New York: Harper & Row, 1967.MATHGoogle Scholar
  22. 22.
    J. Schmitz and R. Williams, “Ground Target Range Extent Analysis Using 1-D HRR Profiles,” SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery VII; Proceedings of SPIE, April 2000, pp. 384-391.Google Scholar
  23. 23.
    D. Gross, J. Schmitz, and R. Williams, “Statistical Analysis of 1-D HRR Target Features,” SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery VII; Proceedings of SPIE, April 2000, pp. 623-630.Google Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • B. S. Denney
    • 1
  • R. J. P. De Figueiredo
    • 1
  1. 1.Neural Computing Systems2081 Business Center DriveIrvineUSA

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