Optimum joint detection and estimation

Abstract

We consider the problem of simultaneous binary hypothesis testing and parameter estimation. By defining suitable joint formulations we develop combined detection and estimation strategies that are optimum. Key point of the proposed methodologies constitutes the fact that they integrate both well known approaches, namely Bayesian and Neyman-Pearson.