Constrained Partially Observed Markov Decision Processes for Adaptive Waveform Scheduling

The dynamic programming approach is applied to a partially observed constrained Markov decision process problem with both total cost and probabilistic criteria. The Markov decision process is partially observed, but it is assumed that the constraint costs are available to the controller, i.e., they are fully observed. The problem is motivated by an adaptive sequential detection application. The application of the dynamic programming results to optimal adaptive truncated sequential detection is demonstrated using examples involving the optimization of radar detection processes.