Ship localization in Santa Barbara Channel using machine learning classifiers

TitleShip localization in Santa Barbara Channel using machine learning classifiers
Publication TypeJournal Article
Year of Publication2017
AuthorsNiu H.Q, Ozanich E., Gerstoft P
JournalJournal of the Acoustical Society of America
Volume142
PaginationEL455-EL460
Date Published2017/11
Type of ArticleArticle
ISBN Number0001-4966
Accession NumberWOS:000416832300005
Keywordsacoustic source localization; inversion; neural-networks
Abstract

Machine learning classifiers are shown to outperform conventional matched field processing for a deep water (600m depth) ocean acoustic-based ship range estimation problem in the Santa Barbara Channel Experiment when limited environmental information is known. Recordings of three different ships of opportunity on a vertical array were used as training and test data for the feed-forward neural network and support vector machine classifiers, demonstrating the feasibility of machine learning methods to locate unseen sources. The classifiers perform well up to 10km range whereas the conventional matched field processing fails at about 4 km range without accurate environmental information. (C) 2017 Acoustical Society of America

DOI10.1121/1.5010064
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