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dc.contributor.advisorRoyset, Johannes O.
dc.contributor.authorMcCray, John P.
dc.dateDec-17
dc.date.accessioned2018-02-07T20:34:05Z
dc.date.available2018-02-07T20:34:05Z
dc.date.issued2017-12
dc.identifier.urihttp://hdl.handle.net/10945/56766
dc.description.abstractSearchers frequently encounter the presence of false targets or clutter, which appears indistinguishable from the real target and must be identified in a second stage of the search. False targets can significantly impede search operations, such as underwater recovery and mine warfare, when contact investigation is costly. Current literature optimizes these searches by applying Bayesian updates to the prior distributions for the real and false targets, in what is called a semi-adaptive search. We take full advantage of intermediate search results, along with soft information about the target, to build up-to-date maximum likelihood estimates of the location of the real target and the distribution of the clutter. Using these estimates in place of the priors, we update and improve the allocation of search effort as the operation progresses. In a detailed simulation study, this new approach increases the probability of finding the target by up to 12% over the optimal semi-adaptive plan without such estimates. These gains are robust to variation in the false target density, time to identify false targets, and total search time available.en_US
dc.description.urihttp://archive.org/details/optimalsemiadapt1094556766
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleOptimal semi-adaptive search with false targetsen_US
dc.typeThesisen_US
dc.contributor.secondreaderSingham, Dashi I.
dc.contributor.departmentOperations Research (OR)
dc.subject.authoroptimal searchen_US
dc.subject.authorsemi-adaptiveen_US
dc.subject.authorfalse targeten_US
dc.subject.authorprobability estimationen_US
dc.description.serviceLieutenant Commander, United States Navyen_US
etd.thesisdegree.nameMaster of Science in Operations Researchen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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