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dc.contributor.authorJain, Ankita Deepak
dc.contributor.authorMakris, Nicholas
dc.date.accessioned2018-04-24T20:04:56Z
dc.date.available2018-04-24T20:04:56Z
dc.date.issued2016-08
dc.date.submitted2016-08
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/1721.1/114950
dc.description.abstractWide area acoustic remote sensing often involves the use of coherent receiver arrays to determine the spatial distribution of sources and scatterers at any instant. The resulting acoustic intensity images are typically corrupted by signal-dependent noise from Gaussian random field fluctuations arising from the central limit theorem and have a spatial resolution that depends on the incident direction, sensing array aperture and wavelength. Here, we use the maximum likelihood method to deconvolve the intensity distribution measured on a coherent line array assuming a discrete angular distribution of incident plane waves. Instantaneous wide area population density images of fish aggregations measured with Ocean Acoustic Waveguide Remote Sensing (OAWRS) are deconvolved to illustrate the effectiveness of this approach in improving angular resolution over conventional planewave beamforming.en_US
dc.description.sponsorshipUnited States. Office of Naval Researchen_US
dc.description.sponsorshipNational Oceanographic Partnership Program (U.S.)en_US
dc.publisherMDPI AGen_US
dc.relation.isversionofhttp://dx.doi.org/10.3390/RS8090694en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceMDPIen_US
dc.titleMaximum Likelihood Deconvolution of Beamformed Images with Signal-Dependent Speckle Fluctuations from Gaussian Random Fields: With Application to Ocean Acoustic Waveguide Remote Sensing (OAWRS)en_US
dc.typeArticleen_US
dc.identifier.citationJain, Ankita, and Nicholas Makris. “Maximum Likelihood Deconvolution of Beamformed Images with Signal-Dependent Speckle Fluctuations from Gaussian Random Fields: With Application to Ocean Acoustic Waveguide Remote Sensing (OAWRS).” Remote Sensing, vol. 8, no. 9, Aug. 2016, p. 694. © 2016 by the Authorsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.mitauthorJain, Ankita Deepak
dc.contributor.mitauthorMakris, Nicholas
dc.relation.journalRemote Sensingen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2018-03-02T14:47:00Z
dspace.orderedauthorsJain, Ankita; Makris, Nicholasen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-4369-296X
mit.licensePUBLISHER_CCen_US


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