Adaptive multiaspect target classification and detection with hidden Markov models

Published

Journal Article

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. © 2005 IEEE.

Full Text

Duke Authors

Cited Authors

  • Ji, S; Liao, X; Carin, L

Published Date

  • October 1, 2005

Published In

Volume / Issue

  • 5 / 5

Start / End Page

  • 1035 - 1042

International Standard Serial Number (ISSN)

  • 1530-437X

Digital Object Identifier (DOI)

  • 10.1109/JSEN.2005.847936

Citation Source

  • Scopus