Title

Exploring Statistic Features for Computationally-Efficient Maximum-Likelihood Algorithms in Signal Processing and Communications Applications

Identifier

etd-03272011-202942

Degree

Doctor of Philosophy (PhD)

Department

Engineering Science (Interdepartmental Program)

Document Type

Dissertation

Abstract

The problem of estimating the parameters in a Gaussian mixture probability density function has been prevalent in the literature for nearly a century. During the last two decades, the method of maximum likelihood has become the predominant approach to this problem. In this thesis work, we try to combat the well-known maximum likelihood (ML) problem by designing the simplified alternative objective functions together with the computationally efficient solutions. Rather than the simple but drawback-prone gradient search algorithms for the ML problems, we propose new iterative procedures for two particular signal processing/communications applications, namely source localization and blind equalization, whose underlying problem is maximum likelihood. Our new iterative methods for solving ML are based on expectation maximization (EM) algorithms. The associated theories and practice including robustness, computational complexity, system performance are also presented in this dissertation.

Date

2010

Document Availability at the Time of Submission

Secure the entire work for patent and/or proprietary purposes for a period of one year. Student has submitted appropriate documentation which states: During this period the copyright owner also agrees not to exercise her/his ownership rights, including public use in works, without prior authorization from LSU. At the end of the one year period, either we or LSU may request an automatic extension for one additional year. At the end of the one year secure period (or its extension, if such is requested), the work will be released for access worldwide.

Committee Chair

Wu,Hsiao-Chun

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