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Multiaspect target identification with wave-based matched pursuits and continuous hidden Markov models

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5 Author(s)
Runkle, P. ; Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA ; Carin, L. ; Couchman, L. ; Yoder, T.J.
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Multiaspect target identification is effected by fusing the features extracted from multiple scattered waveforms; these waveforms are characteristic of viewing the target from a sequence of distinct orientations. Classification is performed in the maximum-likelihood sense, which we show, under reasonable assumptions, can be implemented via a hidden Markov model (HMM). We utilize a continuous-HMM paradigm and compare its performance to its discrete counterpart. The feature parsing is performed via wave-based matched pursuits. Algorithm performance is assessed by considering measured acoustic scattering data from five similar submerged elastic targets

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:21 ,  Issue: 12 )