Multiaspect target detection via the infinite hidden Markov model | Zendy

Kai Ni | Zendy; Yuting Qi | Zendy; Lawrence Carin | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Multiaspect target detection via the infinite hidden Markov model

Author(s) -

Kai Ni,

Yuting Qi,

Lawrence Carin

Publication year - 2007

Publication title -

the journal of the acoustical society of america

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.619

H-Index - 187

eISSN - 1520-8524

pISSN - 0001-4966

DOI - 10.1121/1.2714912

Subject(s) - hierarchical dirichlet process , hidden markov model , gibbs sampling , dirichlet distribution , inference , stochastic matrix , scattering , statistical physics , computer science , set (abstract data type) , matrix (chemical analysis) , markov process , dirichlet process , pattern recognition (psychology) , markov chain , algorithm , mathematics , physics , artificial intelligence , mathematical analysis , machine learning , statistics , bayesian probability , optics , programming language , materials science , composite material , boundary value problem

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research