Robust Implementations of the Multistage Wiener Filter

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dc.contributor.authorHiemstra, John Daviden
dc.contributor.committeechairPratt, Timothy J.en
dc.contributor.committeecochairGoldstein, J. Scotten
dc.contributor.committeememberZoltowski, Michaelen
dc.contributor.committeememberTranter, William H.en
dc.contributor.committeememberDaSilva, Luiz A.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2014-03-14T20:09:10Zen
dc.date.adate2003-04-11en
dc.date.available2014-03-14T20:09:10Zen
dc.date.issued2003-04-04en
dc.date.rdate2004-04-11en
dc.date.sdate2003-04-09en
dc.description.abstractThe research in this dissertation addresses reduced rank adaptive signal processing, with specific emphasis on the multistage Wiener filter (MWF). The MWF is a generalization of the classical Wiener filter that performs a stage-by-stage decomposition based on orthogonal projections. Truncation of this decomposition produces a reduced rank filter with many benefits, for example, improved performance. This dissertation extends knowledge of the MWF in four areas. The first area is rank and sample support compression. This dissertation examines, under a wide variety of conditions, the size of the adaptive subspace required by the MWF (i.e., the rank) as well as the required number of training samples. Comparisons are made with other algorithms such as the eigenvector-based principal components algorithm. The second area investigated in this dissertation concerns "soft stops", i.e., the insertion of diagonal loading into the MWF. Several methods for inserting loading into the MWF are described, as well as methods for choosing the amount of loading. The next area investigated is MWF rank selection. The MWF will outperform the classical Wiener filter when the rank is properly chosen. This dissertation presents six approaches for selecting MWF rank. The algorithms are compared to one another and an overall design space taxonomy is presented. Finally, as digital modelling capabilities become more sophisticated there is emerging interest in augmenting adaptive processing algorithms to incorporate prior knowledge. This dissertation presents two methods for augmenting the MWF, one based on linear constraints and a second based on non-zero weight vector initialization. Both approaches are evaluated under ideal and perturbed conditions. Together the research described in this dissertation increases the utility and robustness of the multistage Wiener filter. The analysis is presented in the context of adaptive array processing, both spatial array processing and space-time adaptive processing for airborne radar. The results, however, are applicable across the entire spectrum of adaptive signal processing applications.en
dc.description.degreePh. D.en
dc.identifier.otheretd-04092003-104445en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-04092003-104445/en
dc.identifier.urihttp://hdl.handle.net/10919/26714en
dc.publisherVirginia Techen
dc.relation.haspartHiemstra_Dissertation_10Apr03.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectreduced ranken
dc.subjectdiagonal loadingen
dc.subjectstopping criteriaen
dc.subjectmultistage Wiener filteren
dc.titleRobust Implementations of the Multistage Wiener Filteren
dc.typeDissertationen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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