Multiaspect target detection via the infinite hidden Markov model

Kai Ni, Yuting Qi, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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. © 2007 Acoustical Society of America.
Original languageEnglish (US)
Pages (from-to)2731-2742
Number of pages12
JournalJournal of the Acoustical Society of America
Volume121
Issue number5
DOIs
StatePublished - May 10 2007
Externally publishedYes

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