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Performance Assessment of a Microwave Tomographic Approach for the Forward Looking Radar Configuration

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Abstract

This paper deals with the application and the performance analysis of a microwave tomography approach for Forward-Looking Radar (FLR) bistatic illumination. The imaging problem is faced by adopting an inverse scattering algorithm based on an approximated model of the electromagnetic scattering. In particular, the Born Approximation is used to describe the wave–material interaction and the targets are assumed to be embedded in a homogenous medium. The adoption of a simplified model of the electromagnetic scattering allows us to analyse how the reconstruction capabilities depend on the measurement configuration. An investigation of the resolution limits in the FLR case is performed and some numerical results are provided in order to show the effectiveness of the proposed approach in cases resembling the ones occurring in real situations.

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References

  1. Balanis, C. (1989). Advanced engineering electromagnetics. New York: Wiley.

    Google Scholar 

  2. Balke, J. (2008) SAR image formation for forward-looking radar receivers in bistatic geometry by airborne illumination, In Proc. IEEE Radar Conf, RADAR ‘08, May 2008, pp. 1–5.

  3. Bertero, M., & Boccacci, P. (1998). Introduction to inverse problems in imaging. Bristol: Institute of Physics Publishing.

    Book  MATH  Google Scholar 

  4. Bradley, M.R., Witten, T.R., Duncan, M., McCummins, R. (2003), Mine detection with a forward-looking ground-penetrating synthetic aperture radar, In Proc. SPIE, 5089, 334–345.

  5. Chew, W. C. (1995). Waves and fields in inhomogeneous media (2nd ed.). New York: IEEE.

    Google Scholar 

  6. Espeter, T., Walterscheid, L., Klare, J., Brenner, A. R., & Ender, J. H. G. (2011). Bistatic forward-looking SAR: Results of a spaceborne-airborne experiment. IEEE Geoscience and Remote Sensing Letters, 8(4), 765–768.

    Article  Google Scholar 

  7. Fortuny-Guasch, J. (2002). A novel 3-D subsurface radar imaging technique. IEEE Transactions on Geoscience and Remote Sensing, 40(2), 443–452.

    Article  Google Scholar 

  8. Franceschetti, G., Iodice, A., & Riccio, D. (2014). Forward-looking synthetic aperture radar (FLoSAR): The array approach. IEEE Geoscience and Remote Sensing Letters, 11(1), 303–307.

    Article  Google Scholar 

  9. Gennarelli, G., & Soldovieri, F. (2013). A linear inverse scattering algorithm for radar imaging in multipath environments. IEEE Geoscience and Remote Sensing Letters, 10(5), 1085–1089.

    Article  Google Scholar 

  10. Giannopoulos, A. (2005). Modelling ground penetrating radar by GprMax. Construction and Building Materials, 19(10), 755–762.

    Article  Google Scholar 

  11. Harrington, R. F. (1993). Field computation by moment methods. New York: Wiley.

    Book  Google Scholar 

  12. Kapoor R., Ressler, M.A., Smith, G. (2000), Forward-looking mine detection using an ultrawideband radar, Proc. SPIE 4038, Detection and remediation technologies for mines and minelike targets V, 1067.

  13. Kositsky, J., Cosgrove, R., Amazeen, C., & Milanfar, P. (2002). Results from a forward-looking GPR mine detection system. Proceedings of SPIE, 4742, 206–217.

    Article  Google Scholar 

  14. Leone, G., & Soldovieri, F. (2003). Analysis of the distorted Born Approximation for subsurface reconstruction: truncation and uncertainties effect. IEEE Transactions on Geoscience and Remote Sensing, 41(1), 66–74.

    Article  Google Scholar 

  15. Persico, R., & Soldovieri, F. (2004). Reconstruction of a slab embedded in a three layered medium from multifrequency data under Born Approximation. Journal of Optical Society of America, Pt. A 21(1), 35–45.

    Article  MathSciNet  Google Scholar 

  16. Persico, R., Bernini, R., & Soldovieri, F. (2005). The role of the measurement configuration in inverse scattering from buried objects under the Born Approximation. IEEE Transactions on Antennas and Propagation, 53(6), 1875–1887.

    Article  Google Scholar 

  17. Persico, R. (2006). On the role of measurement configuration in contactless GPR data processing by means of linear inverse scattering. IEEE Transactions on Antennas and Propagation, 54, 2062–2071.

    Article  Google Scholar 

  18. Persico, R., Soldovieri, F., & Utsi, E. (2010). Microwave tomography for processing of GPR data at Ballachulish. Journal of Geophysics and Engineering, 7(2), 164–173.

    Article  Google Scholar 

  19. Persico, R. (2013). Introduction to ground penetrating radar: inverse scattering and data processing, IEEE Press Series on Electromagnetic Wave Theory. New York: Wiley.

    Google Scholar 

  20. Ren, X., Sun, J., & Yang, R. (2011). A new three-dimensional imaging algorithm for airborne forward-looking SAR. IEEE Geoscience and Remote Sensing Letters, 8(1), 153–157.

    Article  Google Scholar 

  21. Ribalta, A. and M.A. Gonzalez-Huici (2013), Backprojection algorithm for subsurface radar imaging: computing the round-trip time delay. Proceedings of IEEE International Geoscience and Remote Sensing Symposium.

  22. Soldovieri, F., Hugenschmidt, J., Persico, R., & Leone, G. (2007). A linear inverse scattering algorithm for realistic GPR applications. Near Surface Geophysics, 5(1), 29–42.

    Google Scholar 

  23. Soldovieri, F., Lopera, O., & Lambot, S. (2011). Combination of advanced inversion techniques for an accurate target localization via GPR for demining applications. IEEE Transactions on Geoscience and Remote Sensing, 49(1), 451–461.

    Article  Google Scholar 

  24. Tan, W. X., Hong, W., Wang, Y. P., & Wu, Y. R. (2008). 3-D range stacking algorithm for forward-looking SAR 3-D imaging. Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 3, 1212–1215.

    Google Scholar 

  25. Ulander, L. M. H., Hellsten, H., & Stenström, G. (2003). Synthetic-aperture radar processing using fast factorized back-projection. IEEE Transactions on Aerospace and Electronic Systems, 39(3), 760–776.

    Article  Google Scholar 

  26. Walterscheid I., Espeter, T., Klare, J., and Brenner, A. (2010), Bistatic spaceborne-airborne forward-looking SAR, in Proc. EUSAR, Aachen, Germany, pp. 986–989.

  27. Wang, T., Keller, J. M., Gader, P. D., & Sjahputera, O. (2007). Frequency subband processing and feature analysis of forward-looking ground-penetrating radar signals for land-mine detection. IEEE Transactions on Geoscience and Remote Sensing, 45(3), 718–729.

    Article  Google Scholar 

  28. Wang, Y., Li, X., Sun, Y., & Stoica, P. (2005). Adaptive imaging for forward-looking ground penetrating radar. IEEE Transactions on Aerospace and Electronic Systems, 41(3), 922–936.

    Article  Google Scholar 

  29. Zhou, L., Huang, C., & Su, Y. (2012). A fast back-projection algorithm based on cross correlation for GPR imaging. IEEE Geoscience and Remote Sensing Letters, 9(2), 228–232.

    Article  Google Scholar 

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Correspondence to Francesco Soldovieri.

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This article is part of the Topical Collection on Forward-Looking Ground-Penetrating Radar.

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Catapano, I., Soldovieri, F. & González-Huici, M.A. Performance Assessment of a Microwave Tomographic Approach for the Forward Looking Radar Configuration. Sens Imaging 15, 91 (2014). https://doi.org/10.1007/s11220-014-0091-y

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  • DOI: https://doi.org/10.1007/s11220-014-0091-y

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