Multi-aspect acoustic identification of submerged elastic targets via wave-based matching pursuits and continuous hidden Markov models

A wave-based matching-pursuits algorithm is used to parse multi-aspect time-domain backscattering data into its underlying wavefront-resonance constituents, or features. Consequently, the N multi-aspect waveforms under test are mapped into N feature vectors, yn. Target identification is effected by fusing these N vectors in a maximum-likelihood sense, which we show, under reasonable assumptions, can be implemented via a hidden Markov model (HMM). In this paper, we utilize a continuous-HM paradigm, and compare its performance to its discrete counterpart. Algorithm performance is assessed by considering measured acoustic scattering data from five similar submerged elastic targets.