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.

Duke Scholars

Author Lawrence Carin Electrical and Computer Engineering