Adaptive multiaspect target classification and detection with hidden Markov models

• Published in: IEEE sensors journal Vol. 5; no. 5; pp. 1035 - 1042
• Main Authors: Shihao Ji, Xuejun Liao, Carin, L.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2005; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic scattering; Acoustic signal detection; Change detection algorithms; Classification; detection; Entropy; hidden Markov model (HMM); Hidden Markov models; optimal experiments; Performance evaluation; Target recognition; Time measurement; Underwater tracking; Unmanned aerial vehicles
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2005.847936

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.