Joint Detection and Estimation: Optimum Tests and Applications

We consider a well-defined joint detection and parameter estimation problem. By combining the Bayesian formulation of the estimation subproblem with suitable constraints on the detection subproblem, we develop optimum one- and two-step test for the joint detection/estimation setup. The proposed combined strategies have the very desirable characteristic to allow for the trade-off between detection power and estimation quality. Our theoretical developments are, then, applied to the problems of retrospective changepoint detection and multiple-input multiple-output (MIMO) radar. In the former case, we are interested in detecting a change in the statistics of a set of available data and provide an estimate for the time of change, while in the latter in detecting a target and estimating its location. Intense simulations in the MIMO radar example demonstrate that by using jointly optimum schemes, we can experience significant improvement in estimation quality, as compared to generalized the likelihood ratio test or the test that treats the two subproblems separately, with only small sacrifices in detection power.