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

Multiaspect target identification is effected by fusing the features extrac ted from multiple scattered waveforms; these waveforms are characteristic o f viewing the target from a sequence of distinct orientations. Classificati on is performed in the maximum-likelihood sense, which we show. under reaso nable assumptions, can be implemented via a hidden Markov model (HMM). We u tilize a continuous-HMM paradigm and compare its performance to its discret e counterpart. The feature parsing is performed via wave-based matched purs uits. Algorithm performance is assessed by considering measured acoustic sc attering data from five similar submerged elastic targets.