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Frequency estimation using subspace methods
Shatnawi, Heba Awad Addad
Shatnawi, Heba Awad Addad
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2009-05
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Abstract
Complex frequency estimation problem plays a significant role in many engineering
applications. The estimation process was traditionally achieved by the Eigenvalue
Decomposition (EVD) of the spatial correlation matrix of observations. Frequency estimation
has fundamental significant and wide relevance for many reasons. First, any arbitrary signal may
be modeled as a sum of frequencies. Hence, any signal estimation problem may be expressed in
terms of frequency estimation problems. Second, many parameter estimation applications may
be mathematically expressed as a frequency estimation problem.
In this thesis an improved frequency estimation technique is presented based on the
unitary transformation, which was basically applied in the direction of arrival problem. The key
idea of the proposed technique is to convert the complex valued autocorrelation, cumulant, or the
direct data matrix in Hankel like shape into a real valued data matrix with the same dimension.
The resultant real valued matrix will be used to extract the noise and/or the signal subspace
instead of the original complex one. It is well known that real manipulations are easier and faster
than the complex ones.
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Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical and Computer Engineering
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Wichita State University