Document Detail


Application of statistical methods in underwater signal classification.
MedLine Citation:
PMID:  18531415     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
The overall goal of our work is to utilize knowledge of the ocean environment to improve sonar detection and classification performance. Source classification and localization in the underwater environment is a challenging problem in part because propagation through the space- and time-varying medium introduces multipath, variability, and decorrelation to the signal. Traditional underwater signal classification has relied on parametric methods such as the likelihood ratio tests. Recent research has explored non-parametric methods like maximum entropy and maximum likelihood with favorable results. This talk considers other, more contemporary non-parametric methods, e.g. principle component analysis, independent component analysis and support vector machines, and compares their structure and performance with previous results. Work supported by Office of Naval Research Undersea Signal Processing.
Authors:
Brett E Bissinger; Richard Lee Culver; Nirmal K Bose; Colin W Jemmott
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  The Journal of the Acoustical Society of America     Volume:  123     ISSN:  1520-8524     ISO Abbreviation:  J. Acoust. Soc. Am.     Publication Date:  2008 May 
Date Detail:
Created Date:  2008-06-05     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7503051     Medline TA:  J Acoust Soc Am     Country:  United States    
Other Details:
Languages:  eng     Pagination:  3583     Citation Subset:  IM    
Affiliation:
ARL Penn State, PO Box 30, State College, PA 16804, USA, beb194@psu.edu.
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