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dc.contributor.advisorTehernezhadi, Mansouren_US
dc.contributor.authorRavva, Anushaen_US
dc.date.accessioned2018-10-03T15:07:43Z
dc.date.available2018-10-03T15:07:43Z
dc.date.issued2015
dc.identifier.urihttps://commons.lib.niu.edu/handle/10843/18830
dc.descriptionAdvisors: Mansour Tehernezhadi.en_US
dc.descriptionCommittee members: Reza Hashemian; Donald Zinger.en_US
dc.description.abstractA new platform for designing robust adaptive filter is introduced. An adaptive filter is a filter that adjusts its transfer function according to an optimizing adaptive algorithm. The efficiency of the adaptive algorithm being used plays a key role in the working of the adaptive filter. The Least Mean square (LMS) and the Normalized Least Mean square (NLMS) adaptive algorithms are studied. The core part of this research is to use the theory of Kalman filter and use it in adaptive filtering process. The adaptive filtering problem can be updated to a new theory of state estimation problem. The main objective of the research is to evaluate and characterize the efficiency of the adaptive algorithms being used in the adaptive filtering process. The adaptive filtering process will be carried out using different adaptive algorithms and its efficiency is measured in terms of filter convergence speed and the variation in the power of the error signal with changes in the input signal power obtained during the adaptation process. A Kalman based Normalized Least mean square algorithm which is developed outperforms the existing Least Mean square (LMS) and Normalized Least Mean square (NLMS) Algorithms. The simulations are carried out by using MATLAB.en_US
dc.format.extent43 pagesen_US
dc.language.isoengen_US
dc.publisherNorthern Illinois Universityen_US
dc.rightsNIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.en_US
dc.subject.lcshMATLABen_US
dc.subject.lcshElectrical engineeringen_US
dc.subject.lcshKalman filteringen_US
dc.subject.lcshAlgorithmsen_US
dc.titlePerformance analysis of adaptive algorithms and enhancement using Kalman filteren_US
dc.type.genreDissertation/Thesisen_US
dc.typeTexten_US
dc.contributor.departmentDepartment of Electrical Engineeringen_US
dc.description.degreeM.S. (Master of Science)en_US


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