Noise benefits in joint detection and estimation problems

buir.contributor.authorGezici, Sinan
dc.citation.epage247en_US
dc.citation.spage235en_US
dc.citation.volumeNumber118en_US
dc.contributor.authorAkbay, A. B.en_US
dc.contributor.authorGezici, Sinanen_US
dc.date.accessioned2016-02-08T09:33:26Z
dc.date.available2016-02-08T09:33:26Z
dc.date.issued2016en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractAdding noise to inputs of some suboptimal detectors or estimators can improve their performance under certain conditions. In the literature, noise benefits have been studied for detection and estimation systems separately. In this study, noise benefits are investigated for joint detection and estimation systems. The analysis is performed under the Neyman-Pearson (NP) and Bayesian detection frameworks and according to the Bayesian estimation criterion. The maximization of the system performance is formulated as an optimization problem. The optimal additive noise is shown to have a specific form, which is derived under both NP and Bayesian detection frameworks. In addition, the proposed optimization problem is approximated as a linear programming (LP) problem, and conditions under which the performance of the system can or cannot be improved via additive noise are obtained. With an illustrative numerical example, performance comparison between the noise enhanced system and the original system is presented to support the theoretical analysis.en_US
dc.identifier.doi10.1016/j.sigpro.2015.07.009en_US
dc.identifier.issn0165-1684
dc.identifier.urihttp://hdl.handle.net/11693/20702
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.sigpro.2015.07.009en_US
dc.source.titleSignal Processingen_US
dc.subjectDetectionen_US
dc.subjectLinear programmingen_US
dc.subjectNoise enhancementen_US
dc.subjectParameter estimationen_US
dc.titleNoise benefits in joint detection and estimation problemsen_US
dc.typeArticleen_US
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