  We propose a robust likelihood-ratio test (LRT) to detect landmines and
unexploded ordnance using a forward-looking ground-penetrating radar. Instead
of modeling the distributions of the target and clutter returns with parametric
families, we construct a band of feasible probability densities under each
hypothesis. The LRT is then devised based on the least favorable densities
within the bands. This detector is designed to maximize the worst-case
performance over all feasible density pairs and, hence, does not require strong
assumptions about the clutter and noise distributions. The proposed technique
is evaluated using electromagnetic field simulation data of shallow-buried
targets. We show that, compared to detectors based on parametric models, robust
detectors can lead to significantly reduced false alarm rates, particularly in
cases where there is a mismatch between the assumed model and the true
distributions.
