A performance analysis of two linear array processing algorithms for obstacle localization

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buir.contributor.authorArıkan, Orhan
buir.contributor.orcidArıkan, Orhan|0000-0002-3698-8888
dc.citation.epage544en_US
dc.citation.spage533en_US
dc.citation.volumeNumber2561en_US
dc.contributor.authorArıkan, Orhanen_US
dc.contributor.authorBarshan, Billuren_US
dc.coverage.spatialSan Diego, California, U.S.A.en_US
dc.date.accessioned2016-02-08T12:01:04Zen_US
dc.date.available2016-02-08T12:01:04Zen_US
dc.date.issued1995en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 9-14 July 1995en_US
dc.descriptionConference Name: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995en_US
dc.description.abstractThe performance of a commonly employed linear array of sonar sensors is assessed for point- target localization. Two different methods of combining time-of-flight information from the sensors are described to estimate the range and azimuth of the target: pairwise estimate method and the maximum likelihood estimator. The biases and variances of the methods are investigated and their combined effect is compared to the Cramer-Rao Lower Bound. Simulation studies indicate that in estimating range, both methods perform comparably; in estimating azimuth, maximum likelihood estimate is superior at a cost of extra computation.en_US
dc.identifier.doi10.1117/12.217686en_US
dc.identifier.issn0277-786Xen_US
dc.identifier.urihttp://hdl.handle.net/11693/27758en_US
dc.language.isoEnglishen_US
dc.publisherSPIEen_US
dc.relation.isversionofhttps://doi.org/10.1117/12.217686en_US
dc.source.titleProceedings of SPIE Vol. 2561, Signal and Data Processing of Small Targets, 1995en_US
dc.subjectAcoustic sensorsen_US
dc.subjectArray processingen_US
dc.subjectTarget localizationen_US
dc.subjectMaximum likelihood estimateen_US
dc.subjectCramér-Rao Lower Bounden_US
dc.titleA performance analysis of two linear array processing algorithms for obstacle localizationen_US
dc.typeConference Paperen_US

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