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Proceedings Paper

Sparse image reconstruction of targets in multilayered dielectric media using total variation minimization
Author(s): Wenji Zhang; Ahmad Hoorfar
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Paper Abstract

In this paper, we present a sparse image reconstruction approach for radar imaging through multilayered media with total variation minimization (TVM). The approach is well suited for high-resolution imaging for both ground penetrating radar (GPR) and through-the-wall radar imaging (TWRI) applications. The multilayered media Green’s function is incorporated in the imaging algorithm to efficiently model the wave propagation in the multilayered environment. For GPR imaging, the multilayered subsurface Green’s function is derived in closed form with saddle point method, which is significantly less time consuming than numerical methods. For through-the-wall radar imaging, where the first and last layers are freespace, a far field approximation of the Green’s function in analytical form is used to model the wave propagation through single or multilayered building walls. The TVM minimizes the gradient of the image resulting in excellent edge preservation and shape reconstruction of the image. Representative examples are presented to show high quality imaging results with limited data under various subsurface and through-the-wall imaging scenarios.

Paper Details

Date Published: 5 May 2017
PDF: 7 pages
Proc. SPIE 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, 1021105 (5 May 2017); doi: 10.1117/12.2261777
Show Author Affiliations
Wenji Zhang, Villanova Univ. (United States)
Ahmad Hoorfar, Villanova Univ. (United States)

Published in SPIE Proceedings Vol. 10211:
Compressive Sensing VI: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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