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dc.contributor.authorSadler, Brian M.en_US
dc.contributor.authorCasey, Stephen D.en_US
dc.date.accessioned2007-05-23T10:01:15Z
dc.date.available2007-05-23T10:01:15Z
dc.date.issued1996en_US
dc.identifier.urihttp://hdl.handle.net/1903/5736
dc.description.abstractAnalysis of periodic pulse trains based on time of arrival is considered, with perhaps very many missing observations and contaminated data. A period estimator is developed based on a modified Euclidean algorithm. This algorithm is a computationally simple, robust method for estimating the greatest common divisor of a noisy contaminated data set. The resulting estimate, while not maximum likelihood, is used as initialization in a three-step algorithm that achieves the Cramer-Rao bound for moderate noise levels, as shown by comparing Monte Carlo results with the Cramer-Rao bounds. An extension using multiple independent data records is also developed that overcomes high levels of contamination.en_US
dc.format.extent1123542 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1996-6en_US
dc.subjectdigital communicationsen_US
dc.subjectestimationen_US
dc.subjectfilteringen_US
dc.subjectrobust information processingen_US
dc.subjectsignal processingen_US
dc.subjectparameter estimation of pulse trainsen_US
dc.subjectnumber theoryen_US
dc.subjectharmonic analysisen_US
dc.subjectIntelligent Control Systemsen_US
dc.titleOn Periodic Pulse Interval Analysis with Outliers and Missing Observationsen_US
dc.typeTechnical Reporten_US
dc.contributor.departmentISRen_US


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