Dual hidden Markov model characterization of wavelet coefficients from multi-aspect scattering data
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.
Dasgupta, N; Runkle, P; Couchman, L; Carin, L
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