Dual hidden Markov model characterization of wavelet coefficients from multi-aspect scattering data

Published

Journal Article

We consider angle-dependent scattering (electromagnetic or acoustic) from a general target, for which the scattered signal is a non-stationary function of the target-sensor orientation. A statistical model is presented for the wavelet coefficients of such a signal, in which the angular non-stationary is characterized by an 'outer' hidden Markov model (HMMo). The statistics of the wavelet coefficients, within a state of the outer HMM, are characterized by a second, 'inner' HMM (HMMi), exploiting the tree structure of the wavelet decomposition. This dual-HMM construct is demonstrated by considering multi-aspect target identification using measured acoustic scattering data.

Full Text

Duke Authors

Cited Authors

  • Dasgupta, N; Runkle, P; Couchman, L; Carin, L

Published Date

  • January 1, 2000

Published In

Volume / Issue

  • 4038 (II) /

Start / End Page

  • 954 - 965

International Standard Serial Number (ISSN)

  • 0277-786X

Digital Object Identifier (DOI)

  • 10.1117/12.396179

Citation Source

  • Scopus