Forward Looking GPR-Based Landmine Detection Using a Robust Likelihood Ratio Test

We propose a robust likelihood ratio test to detect landmines using forward-looking ground penetrating radar. Instead of modeling the distributions of the target and clutter returns with parametric families, we use a kernel density estimator to construct a band of feasible probability densities under each hypothesis. The likelihood ratio test is then devised based on the least favorable densities within the bands. This detector is designed to maximize the worst-case performance over all the feasible density pairs and, hence, does not require strong assumptions about the clutter and noise distributions.