Nonmyopic Multiaspect Sensing With Partially Observable Markov Decision Processes
We consider the problem of sensing a concealed or distant target by interrogation from multiple sensors situated on a single platform. The available actions that may be taken are selection of the next relative target-platform orientation and the next sensor to be deployed. The target is modeled in terms of a set of states, each state representing a contiguous set of target-sensor orientations over which the scattering physics is relatively stationary. The sequence of states sampled at multiple target-sensor orientations may be modeled as a Markov process. The sensor only has access to the scattered fields, without knowledge of the particular state being sampled, and, therefore, the problem is modeled as a partially observable Markov decision process (POMDP). The POMDP yields a policy, in which the belief state at any point is mapped to a corresponding action. The nonmyopic policy is compared to an approximate myopic approach, with example results presented for measured underwater acoustic scattering data