Maximum likelihood time-domain beamforming using simulated annealing

dc.contributor.author Xu, Kevin
dc.date.accessioned 2011-07-07T20:14:50Z
dc.date.available 2011-07-07T20:14:50Z
dc.date.issued 1999-09
dc.description Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 1999 en_US
dc.description.abstract An algorithm is developed for underwater acoustic signal processing with an array of hydrophones. With various acoustic signals coming from different directions, the maximum likelihood approach is used to estimate the source bearings and time series. Simulated annealing is used to implement the resulting time-domain beamformer. Broadband signals in spatially correlated noise are treated. Previous time-domain beamformers did not consider the correlation between random noise, and they did not use the concept of maximum likelihood, which is asymptotically optimal. We show that improved resolution can be achieved using this new method. en_US
dc.format.mimetype application/pdf
dc.identifier.citation Xu, K. (1999). Maximum likelihood time-domain beamforming using simulated annealing [Doctoral thesis, Massachusetts Institute of Technology and Woods Hole Oceanographic Institution]. Woods Hole Open Access Server. https://doi.org/10.1575/1912/4656
dc.identifier.doi 10.1575/1912/4656
dc.identifier.uri https://hdl.handle.net/1912/4656
dc.language.iso en_US en_US
dc.publisher Massachusetts Institute of Technology and Woods Hole Oceanographic Institution en_US
dc.relation.ispartofseries WHOI Theses en_US
dc.subject Underwater acoustics en_US
dc.subject Signal processing en_US
dc.subject Simulated annealing en_US
dc.subject Time-domain analysis en_US
dc.title Maximum likelihood time-domain beamforming using simulated annealing en_US
dc.type Thesis en_US
dspace.entity.type Publication
relation.isAuthorOfPublication 0d84e3cb-ea88-4029-9c4b-90523b9dd56c
relation.isAuthorOfPublication.latestForDiscovery 0d84e3cb-ea88-4029-9c4b-90523b9dd56c
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