An MM-based algorithm for L1-regularized least squares estimation in GPR image reconstruction

Author

Ndoye, Mandoye ; Anderson, John M. M.

Author_Institution

Dept. of Electr. & Comput. Eng., Howard Univ., Washington, DC, USA

fYear

2013

fDate

April 29 2013-May 3 2013

Firstpage

1

Lastpage

6

Abstract

We present a regularized Least-Squares (LS) method for estimating subsurface reflection coefficients from ground penetrating radar (GPR) measurements. The expected sparsity of the reflection coefficients induced via L₁-regularization. The majorize-minimize (MM) framework is used to develop a novel iterative algorithm for solving the L₁-regularized LS problem in a straightforward and effective manner. The proposed L₁-regularized Least-Squares (L₁-LS) algorithm is amenable to parallel implementation since the MM procedure decouples the estimation of the individual reflection coefficients. In order to work toward an extended algorithm that would be suited for real-time implementations, we investigate an online strategy for choosing the regularization parameter. The L₁-LS algorithm is validated using simulated GPR datasets.

Keywords

ground penetrating radar; image reconstruction; iterative methods; least squares approximations; GPR image reconstruction; GPR measurements; L₁ regularization; L₁ regularized LS problem; L₁-regularized least squares estimation; L₁-regularized least-squares algorithm; MM procedure; MM-based algorithm; ground penetrating radar measurements; iterative algorithm; majorize-minimize framework; reflection coefficients; regularization parameter; regularized least-squares method; simulated GPR datasets; subsurface reflection coefficients; Receivers;

fLanguage

English

Publisher

ieee

Conference_Titel

Radar Conference (RADAR), 2013 IEEE

Conference_Location

Ottawa, ON

ISSN

1097-5659

Print_ISBN

978-1-4673-5792-0

Type

conf

DOI

10.1109/RADAR.2013.6586138

Filename

6586138