Statistical Models for Landmine Detection in Ground Penetrating Radar: Applications to Synthetic Data Generation and Pre-Screening

As ground penetrating radar phenomenology continues to improve, more advanced statistical signal processing approaches become applicable to subsurface inference in GPR data. Despite the wide body of literature exploring the applications of various approaches to processing GPR data, statistical modeling of realistic soil responses is a difficult task, and the algorithms developed for real-time fielded GPR processing are rarely directly motivated by statistical models of GPR data. In this work, we present a tractable spatial statistical model for volumetric GPR data which can be used to motivate the application of various signal processing approaches to solving problems of interest in GPR data like pre-screening, feature extraction, and air/ground response tracking.