Contributions to Robust Adaptive Signal Processing with Application to Space-Time Adaptive Radar

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dc.contributor.authorSchoenig, Gregory Neumannen
dc.contributor.committeechairMili, Lamine M.en
dc.contributor.committeememberPicciolo, Michael L.en
dc.contributor.committeememberSpitzner, Dan J.en
dc.contributor.committeememberZaghloul, Amir I.en
dc.contributor.committeememberBeex, A. A. Louisen
dc.contributor.committeememberGoldstein, J. Scotten
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2014-03-14T20:09:58Zen
dc.date.adate2007-05-04en
dc.date.available2014-03-14T20:09:58Zen
dc.date.issued2007-04-12en
dc.date.rdate2007-05-04en
dc.date.sdate2007-04-18en
dc.description.abstractClassical adaptive signal processors typically utilize assumptions in their derivation. The presence of adequate Gaussian and independent and identically distributed (i.i.d.) input data are central among such assumptions. However, classical processors have a tendency to suffer a degradation in performance when assumptions like these are violated. Worse yet, such degradation is not guaranteed to be proportional to the level of deviation from the assumptions. This dissertation proposes new signal processing algorithms based on aspects of modern robustness theory, including methods to enable adaptivity of presently non-adaptive robust approaches. The contributions presented are the result of research performed jointly in two disciplines, namely robustness theory and adaptive signal processing. This joint consideration of robustness and adaptivity enables improved performance in assumption-violating scenarios—scenarios in which classical adaptive signal processors fail. Three contributions are central to this dissertation. First, a new adaptive diagnostic tool for high-dimension data is developed and shown robust in problematic contamination. Second, a robust data-pre-whitening method is presented based on the new diagnostic tool. Finally, a new suppression-based robust estimator is developed for use with complex-valued adaptive signal processing data. To exercise the proposals and compare their performance to state- of-the art methods, data sets commonly used in statistics as well as Space-Time Adaptive Processing (STAP) radar data, both real and simulated, are processed, and performance is subsequently computed and displayed. The new algorithms are shown to outperform their state-of-the-art counterparts from both a signal-to-interference plus noise ratio (SINR) convergence rate and target detection perspective.en
dc.description.degreePh. D.en
dc.identifier.otheretd-04182007-170510en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-04182007-170510/en
dc.identifier.urihttp://hdl.handle.net/10919/26972en
dc.publisherVirginia Techen
dc.relation.haspartDissertation_Schoenig.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectGM-estimatoren
dc.subjectSINR Convergenceen
dc.subjectAdaptive Signal Processingen
dc.subjectRobusten
dc.titleContributions to Robust Adaptive Signal Processing with Application to Space-Time Adaptive Radaren
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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