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

Nilanjan Dasgupta; Paul Runkle; Luise Couchman; Lawrence Carin

doi:10.1016/S0165-1684(00)00262-0

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 journal › Article › peer-review

28 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 language	English (US)
Pages (from-to)	1303-1316
Number of pages	14
Journal	Signal Processing
Volume	81
Issue number	6
DOIs	https://doi.org/10.1016/S0165-1684(00)00262-0
State	Published - Jun 1 2001
Externally published	Yes

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10.1016/S0165-1684(00)00262-0

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title = "Dual hidden Markov model for characterizing wavelet coefficients from multi-aspect scattering data",

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. {\textcopyright} 2001 Elsevier Science B.V.",

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AU - Dasgupta, Nilanjan

AU - Runkle, Paul

AU - Couchman, Luise

AU - Carin, Lawrence

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Y1 - 2001/6/1

N2 - 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.

AB - 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.

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DO - 10.1016/S0165-1684(00)00262-0

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VL - 81

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JO - Signal Processing

JF - Signal Processing

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ER -

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

Abstract

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