Robust signal detection with nonstandard decision regions

A reality faced in the practical application of signal detection is the inexact statistical knowledge of the underlying random processes. Accordingly, it is often desirable for a detector to possess robustness. In this paper, we review how the concept of manifold slope can be employed to admit the measurement of robustness thus allowing the degree of robustness to be a factor in the design of the signal detector. We then present new results that show how certain nonstandard decision regions can result in what we term 'negative boundaries' which have the potential to enhance robustness. An example of this approach is provided and the results compared to the classical Huber approach for robust detection.