Multiaspect target detection via the infinite hidden Markov model.

Published

Journal Article

A new multiaspect target detection method is presented based on the infinite hidden Markov model (iHMM). The scattering of waves from a target is modeled as an iHMM with the number of underlying states treated as infinite, from which a full posterior distribution on the number of states associated with the targets is inferred and the target-dependent states are learned collectively. A set of Dirichlet processes (DPs) are used to define the rows of the HMM transition matrix and these DPs are linked and shared via a hierarchical Dirichlet process. Learning and inference for the iHMM are based on a Gibbs sampler. The basic framework is applied to a detailed analysis of measured acoustic scattering data.

Full Text

Duke Authors

Cited Authors

  • Ni, K; Qi, Y; Carin, L

Published Date

  • May 2007

Published In

Volume / Issue

  • 121 / 5 Pt1

Start / End Page

  • 2731 - 2742

PubMed ID

  • 17550173

Pubmed Central ID

  • 17550173

Electronic International Standard Serial Number (EISSN)

  • 1520-8524

International Standard Serial Number (ISSN)

  • 0001-4966

Digital Object Identifier (DOI)

  • 10.1121/1.2714912

Language

  • eng