Adaptive multiaspect target classification and detection with hidden Markov models

Shihao Ji, Xuejun Liao, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

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. © 2005 IEEE.
Original languageEnglish (US)
Pages (from-to)1035-1042
Number of pages8
JournalIEEE Sensors Journal
Volume5
Issue number5
DOIs
StatePublished - Oct 1 2005
Externally publishedYes

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