Title :
An Iterative ℓ1-regularized least absolute deviation algorithm for robust GPR Imaging
Author :
Ndoye, Mandoye ; Anderson, John M. M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Howard Univ., Washington, DC, USA
Abstract :
We present an ℓ1-regularized least absolute deviation (ℓ1-LAD) algorithm for estimating subsurface reflection coefficients from ground penetrating radar (GPR) measurements. The ℓ1-regularization incorporates the known sparsity of the reflection coefficients for typical scenes, while the LAD criteria provides robustness against potential outliers/spikes in the data. The majorize-minimize (MM) principle is used to solve the ℓ1-LAD optimization problem and the resulting iterative algorithm is straightforward to implement and computationally efficient with judicious data processing and/or parallelization. The ℓ1-LAD algorithm is amenable to parallelization because the MM procedure decouples the estimation of the reflection coefficients. The robustness and effectiveness of the proposed ℓ1-LAD algorithm is validated using a 1-D time series and simulated GPR dataset.
Keywords :
ground penetrating radar; optimisation; radar imaging; time series; ℓ1-LAD algorithm; ℓ1-LAD optimization problem; 1D time series; LAD criteria; MM procedure; ground penetrating radar measurements; iterative ℓ1-regularized least absolute deviation; majorize-minimize principle; robust GPR imaging; simulated GPR dataset; subsurface reflection coefficients; Apertures; Ground penetrating radar; Optimization; Robustness; ground penetrating radar; least absolute deviation; majorize-minimize; sparsity;
Conference_Titel :
Information Sciences and Systems (CISS), 2014 48th Annual Conference on
Conference_Location :
Princeton, NJ
DOI :
10.1109/CISS.2014.6814099
