Evaluation of a model based inversion algorithm for GPR signal processing with correlation for target classification

This paper evaluates a non-intrusive buried object classifier developed for a ground penetration radar (GPR) system. The process uses a model based inversion algorithm to generate synthetic data sets which are correlated with real data sets. Recent work has introduced this technique to the community. Accomplishments and deficiencies with the procedure are discussed. Real data sets were collected with a commercially available GPR that is used to locate buried objects in a non-invasive manner. While synthetic data has been generated with a software implementation of a mathematical model developed for electromagnetic returns from a buried object. These real and synthetic measurements have been processed and compared using this technique to measure the simularities and the differences between the processed data sets. The processed synthetic data images exhibited similar traits as present in the processed real data. Favorable visible correlation results were observed, yet the analytical comparisons were not conclusive due to lack of adequate data.