Dual hidden Markov model for characterizing wavelet coefficients from multi-aspect scattering data

Nilanjan Dasgupta, Paul Runkle, Luise Couchman, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Angle-dependent scattering (electromagnetic or acoustic) is considered 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-stationarity 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" 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. © 2001 Elsevier Science B.V.
Original languageEnglish (US)
Pages (from-to)1303-1316
Number of pages14
JournalSignal Processing
Volume81
Issue number6
DOIs
StatePublished - Jun 1 2001
Externally publishedYes

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