TY - JOUR
T1 - Dual hidden Markov model for characterizing 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 - 2001/6/1
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.
UR - https://linkinghub.elsevier.com/retrieve/pii/S0165168400002620
UR - http://www.scopus.com/inward/record.url?scp=0035368531&partnerID=8YFLogxK
U2 - 10.1016/S0165-1684(00)00262-0
DO - 10.1016/S0165-1684(00)00262-0
M3 - Article
SN - 0165-1684
VL - 81
SP - 1303
EP - 1316
JO - Signal Processing
JF - Signal Processing
IS - 6
ER -