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• W2275178909 abstract "Complex frequency estimation problem plays a significant role in many engineeringapplications. The estimation process was traditionally achieved by the EigenvalueDecomposition (EVD) of the spatial correlation matrix of observations. Frequency estimationhas fundamental significant and wide relevance for many reasons. First, any arbitrary signal maybe modeled as a sum of frequencies. Hence, any signal estimation problem may be expressed interms of frequency estimation problems. Second, many parameter estimation applications maybe mathematically expressed as a frequency estimation problem.In this thesis an improved frequency estimation technique is presented based on theunitary transformation, which was basically applied in the direction of arrival problem. The keyidea of the proposed technique is to convert the complex valued autocorrelation, cumulant, or thedirect 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 subspaceinstead of the original complex one. It is well known that real manipulations are easier and fasterthan the complex ones." @default.
• W2275178909 created "2016-06-24" @default.
• W2275178909 creator A5090297093 @default.
• W2275178909 date "2010-10-06" @default.
• W2275178909 modified "2023-09-24" @default.
• W2275178909 title "FREQUENCY ESTIMATION: USING SUBSPACE METHODS" @default.
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• W2275178909 hasPublicationYear "2010" @default.
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