Adaptive multi-aspect target classification and detection with hidden Markov models

We consider target classification and detection based on back-scattered observations measured from a sequence of target-sensor orientations. The multi-aspect scattered waves from a given target are modeled with a hidden Markov model (HMM). The targets are assumed concealed and the absolute target-sensor orientation is assumed unknown; therefore, it is only possible to control the angular displacements (change in orientation) between consecutive measurements. The performance of the HMM classifiers/detectors is influenced by the choice of the angular displacements, the optimization of which motivates adaptive search strategies developed in this paper, based on entropy-driven optimality criteria. The search proceeds in a sequential fashion. Based on the previous observations and their associated angular displacements, one determines the optimal next displacement to perform an associated observation. The search strategies are detailed and example results presented on adaptive classification and detection of underwater targets.