Globally convergent and adaptive finite element methods in imaging of buried objects from experimental backscattering radar measurements

Larisa Beilina, Nguyen Trung Thành, Michael V. Klibanov, John Bondestam Malmberg

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Abstract We consider a two-stage numerical procedure for imaging of objects buried in dry sand using time-dependent backscattering experimental radar measurements. These measurements are generated by a single point source of electric pulses and are collected using a microwave scattering facility which was built at the University of North Carolina at Charlotte. Our imaging problem is formulated as the inverse problem of the reconstruction of the spatially distributed dielectric constant εr(x),x ∈ R3, which is an unknown coefficient in Maxwell's equations. On the first stage the globally convergent method of Beilina and Klibanov (2012) is applied to get a good first approximation for the exact solution. Results of this stage were presented in Thành et al. (2014). On the second stage the locally convergent adaptive finite element method of Beilina (2011) is applied to refine the solution obtained on the first stage. The two-stage numerical procedure results in accurate imaging of all three components of interest of targets: shapes, locations and refractive indices. In this paper we briefly describe methods and present new reconstruction results for both stages.

Original languageEnglish (US)
Article number9898
Pages (from-to)371-391
Number of pages21
JournalJournal of Computational and Applied Mathematics
Volume289
DOIs
StatePublished - Jun 3 2015

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Globally convergent and adaptive finite element methods in imaging of buried objects from experimental backscattering radar measurements'. Together they form a unique fingerprint.

Cite this