Constraints implementation for IQML and mode direction-of-arrival estimators

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorAlves, Carlos Antonio
dc.creatorColares, Ricardo Fialho
dc.date2014-05-27T11:19:59Z
dc.date2016-10-25T18:16:42Z
dc.date2014-05-27T11:19:59Z
dc.date2016-10-25T18:16:42Z
dc.date2000-12-01
dc.date.accessioned2017-04-06T00:58:14Z
dc.date.available2017-04-06T00:58:14Z
dc.identifierMidwest Symposium on Circuits and Systems, v. 3, p. 1418-1421.
dc.identifierhttp://hdl.handle.net/11449/66341
dc.identifierhttp://acervodigital.unesp.br/handle/11449/66341
dc.identifier10.1109/MWSCAS.2000.951479
dc.identifierWOS:000172099300328
dc.identifier2-s2.0-0034464891
dc.identifierhttp://dx.doi.org/10.1109/MWSCAS.2000.951479
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/887924
dc.descriptionThe iterative quadratic maximum likelihood IQML and the method of direction estimation MODE are well known high resolution direction-of-arrival DOA estimation methods. Their solutions lead to an optimization problem with constraints. The usual linear constraint presents a poor performance for certain DOA values. This work proposes a new linear constraint applicable to both DOA methods and compare their performance with two others: unit norm and usual linear constraint. It is shown that the proposed alternative performs better than others constraints. The resulting computational complexity is also investigated.
dc.languageeng
dc.relationMidwest Symposium on Circuits and Systems
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectComputational complexity
dc.subjectConstraint theory
dc.subjectIterative methods
dc.subjectMathematical models
dc.subjectMatrix algebra
dc.subjectMaximum likelihood estimation
dc.subjectOptimization
dc.subjectDirection-of-arrival estimators
dc.subjectIterative quadratic maximum likelihood estimation
dc.subjectLinear constraint
dc.subjectSignal processing
dc.titleConstraints implementation for IQML and mode direction-of-arrival estimators
dc.typeOtro


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