Adaptive multiaspect target classification and detection with hidden Markov models

Target detection and classification are considered based on backscattered signals observed from a sequence of target-sensor orientations, with the measurements performed as a function of orientation (angle) at a fixed range. The theory of optimal experiments is applied to adaptively optimize the sequence of target-sensor orientations considered. This is motivated by the fact that if fewer, better-chosen measurements are used then targets can be recognized more accurately with less time and expense. Specifically, based on the previous sequence of observations O/sub t/={O/sub 1/,...,O/sub t/}, the technique determines what change in relative target-sensor orientation /spl Delta//spl theta//sub t+1/ is optimal for performing measurement t+1, to yield observation O/sub t+1/. The target is assumed distant or hidden, and, therefore, the absolute target-sensor orientation is unknown. We detail the adaptive-sensing algorithm, employing a hidden Markov model representation of the multiaspect scattered fields, and example classification and detection results are presented for underwater targets using acoustic scattering data.