Title :
Multicomponent signal classification using the PMHT algorithm
Author :
Ainsleigh, Phillip ; Luginbuhl, Tod
Author_Institution :
Naval Undersea Warfare Center, Newport, RI, USA
Abstract :
The probabilistic multi-hypothesis tracking (PMHT) algorithm is extended for application to classification. The PMHT model is reformulated as a bank of continuous-state hidden Markov models, allowing for supervised learning of the class-conditional probability density models, and for likelihood evaluation of multicomponent signals.
Keywords :
hidden Markov models; learning (artificial intelligence); pattern classification; tracking; PMHT; class-conditional; continuous-state hidden Markov models; likelihood evaluation; probabilistic multi-hypothesis tracking algorithm; probability density models; supervised learning; Condition monitoring; Hidden Markov models; Pattern classification; Probability density function; Supervised learning; Target tracking; Time measurement; Trajectory; Underwater acoustics; Underwater tracking;
Conference_Titel :
Information Fusion, 2002. Proceedings of the Fifth International Conference on
Conference_Location :
Annapolis, MD, USA
Print_ISBN :
0-9721844-1-4
DOI :
10.1109/ICIF.2002.1021230
