On the optimality of data processors for signal detection over a class of contaminated noises

We consider the effect induced on the data processor of a signal detection system when the underlying noise distribution functions are varied about their nominal values. We first consider the detection of a time varying deterministic signal in additive noise, and then extend our results to a more general situation in which the signal possesses a random amplitude. Our results characterize a class of contaminants of an arbitrary nominal distribution over which the data processor can be designed using the nominal distribution, and it is seen, for example, that such a class can contain distribution functions which can differ greatly from the nominal distribution.