Alternate Title

Inverse method for characterization of buried anomaly


This work addresses the subsurface imaging of the buried lossy dielectric objects embedded in the dispersive lossy background with unknown rough surface using the data collected by ground penetrated radar. The applications of this problem range from environmental monitoring to nondestructive testing. An iterative inversion algorithm is proposed to reconstruct the geometry and dielectric parameters of the half-space ground as well as the buried object. This approach utilizes the efficient numerical Levenberg-Marquardt algorithm to update the unknowns from a judicious initial guess of boundaries modeled by parametric B-spline curves as well as dielectric parameters. Our approach is based on the new Semi-Analytic Mode Matching (SAMM) forward model which is a fast and efficient model to determine the scattered electromagnetic fields from the rough surface and the object. The numerical experiments involving 2-D geometries and TM incident plane-waves demonstrate the accuracy and reliability of our inverse method.


Poster presented at the 2006 Thrust R2A Multi-View Tomographic Methods Conference


random rough surface, lossy dielectric objects, SAMM, TM

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Imaging systems




Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems (Gordon-CenSSIS)

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Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems (Gordon-CenSSIS)

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