CRLB based optimal noise enhanced parameter estimation using quantized observations

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buir.contributor.authorGezici, Sinan
dc.citation.epage480en_US
dc.citation.issueNumber5en_US
dc.citation.spage477en_US
dc.citation.volumeNumber17en_US
dc.contributor.authorBalkan, G. O.en_US
dc.contributor.authorGezici, Sinanen_US
dc.date.accessioned2016-02-08T09:59:01Z
dc.date.available2016-02-08T09:59:01Z
dc.date.issued2010-02-22en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractIn this letter, optimal additive noise is characterized for parameter estimation based on quantized observations. First, optimal probability distribution of noise that should be added to observations is formulated in terms of a CramerRao lower bound (CRLB) minimization problem. Then, it is proven that optimal additive noise can be represented by a constant signal level, which means that randomization of additive signal levels is not needed for CRLB minimization. In addition, the results are extended to the cases in which there exists prior information about the unknown parameter and the aim is to minimize the Bayesian CRLB (BCRLB). Finally, a numerical example is presented to explain the theoretical results.en_US
dc.identifier.doi10.1109/LSP.2010.2043787en_US
dc.identifier.issn1070-9908
dc.identifier.urihttp://hdl.handle.net/11693/22353
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/LSP.2010.2043787en_US
dc.source.titleIEEE Signal Processing Lettersen_US
dc.subjectCramer-Rao lower bounden_US
dc.subjectEstimationen_US
dc.subjectNoise enhanced estimationen_US
dc.subjectQuantizationen_US
dc.titleCRLB based optimal noise enhanced parameter estimation using quantized observationsen_US
dc.typeArticleen_US

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