Multiaspect Target Identification with Wave-Based Matched Pursuits and Continuous Hidden Markov Models

Authors:
Paul Runkle;Lawrence Carin;Luise Couchman;Joseph A. Bucaro;Timothy J. Yoder
Affiliations:
Duke Univ., Durham, NC;Duke Univ., Durham, NC;Naval Research Lab., Washington, D.C;Naval Research Lab., Washington, D.C;SFA, Inc., Largo, MD
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1999

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Abstract

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.