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

Nilanjan Dasgupta; Paul Runkle; Luise Couchman; Lawrence Carin

doi:10.1117/12.396179

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

Nilanjan Dasgupta, Paul Runkle, Luise Couchman, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings of SPIE - The International Society for Optical Engineering
Publisher	Society of Photo-Optical Instrumentation EngineersBellingham
Pages	954-965
Number of pages	12
DOIs	https://doi.org/10.1117/12.396179
State	Published - Jan 1 2000
Externally published	Yes

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10.1117/12.396179

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

abstract = "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.",

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Dual hidden Markov model characterization of wavelet coefficients from multi-aspect scattering data. / Dasgupta, Nilanjan; Runkle, Paul; Couchman, Luise et al.
Proceedings of SPIE - The International Society for Optical Engineering. Society of Photo-Optical Instrumentation EngineersBellingham, 2000. p. 954-965.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Dual hidden Markov model characterization of wavelet coefficients from multi-aspect scattering data

AU - Dasgupta, Nilanjan

AU - Runkle, Paul

AU - Couchman, Luise

AU - Carin, Lawrence

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09

PY - 2000/1/1

Y1 - 2000/1/1

N2 - 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.

AB - 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.

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BT - Proceedings of SPIE - The International Society for Optical Engineering

PB - Society of Photo-Optical Instrumentation EngineersBellingham

ER -

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

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