Title :
Adaptive multiaspect target classification and detection with hidden Markov models
Author :
Ji, Shihao ; Liao, Xuejun ; Carin, Lawrence
Author_Institution :
Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA
Abstract :
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.
Keywords :
hidden Markov models; object detection; pattern classification; target tracking; adaptive multiaspect target classification; adaptive multiaspect target detection; adaptive-sensing algorithm; backscattered signals; entropy; hidden Markov model; multiaspect scattered field; target-sensor orientation; Acoustic scattering; Acoustic signal detection; Change detection algorithms; Entropy; Hidden Markov models; Performance evaluation; Target recognition; Time measurement; Underwater tracking; Unmanned aerial vehicles; Classification; detection; entropy; hidden Markov model (HMM); optimal experiments;
Journal_Title :
Sensors Journal, IEEE
DOI :
10.1109/JSEN.2005.847936