Robust detection under Bhattacharyya metric

In a variety of detection applications, robust techniques are used to cope with the uncertainty in the statistical model assumed for the data. Traditional methods using ε-contamination classes are often too restrictive. Other techniques require that the nominal densities be Gaussian. This paper proposes Bhattacharyya balls around arbitrary nominal distributions as a flexible yet realistic alternative in uncertainty modeling. We derive probability densities that are least discriminable in the Bhattacharyya metric.