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dc.contributor.advisorParker, Sydney R.
dc.contributor.authorKo, Soon-Ju
dc.dateDecember 1977
dc.date.accessioned2012-11-16T19:13:42Z
dc.date.available2012-11-16T19:13:42Z
dc.date.issued1977-12
dc.identifier.urihttp://hdl.handle.net/10945/18004
dc.description.abstractAn adaptive recursive digital filter is presented in which feedback and feedforward gains are adjusted adaptively to minimize a least square performance function on a sliding window averaging process. A two-dimensional version of the adaptive filter is developed and its performance compared with the optimal Wiener filter. The filter is shown to be effective in separating three diagonal trajectory streaks from a background of correlated noise added to white noise. Although the recursive adaptive filter approaches the optimal Wiener filter in performance, it does not require a priori statistical knowledge as does the Wiener filter to which it is compared. The results indicate that the recursive adaptive filter "learns" the statistics and adapts.
dc.description.urihttp://archive.org/details/andaptiverecursi1094518004
dc.language.isoen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsCopyright is reserved by the copyright owner
dc.titleAn adaptive recursive filter.en_US
dc.typeThesisen_US
dc.contributor.secondreaderKirk, Donald E.
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.contributor.departmentElectrical Engineering
dc.description.serviceLieutenant, Republic of Korea Navy
etd.thesisdegree.nameDegree of Electrical Engineeren_US
etd.thesisdegree.levelProfessional Degreeen_US
etd.thesisdegree.disciplineElectrical Engineeringen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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