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Adaptive multiaspect target classification and detection with hidden Markov models

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3 Author(s)
Shihao Ji ; Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA ; Xuejun Liao ; Carin, L.

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 Ot={O1,...,Ot}, the technique determines what change in relative target-sensor orientation Δθt+1 is optimal for performing measurement t+1, to yield observation Ot+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.

Published in:

Sensors Journal, IEEE  (Volume:5 ,  Issue: 5 )