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Spectral Analysis of Signals: The Missing Data Case - , Jian Li

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Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive filtering based techniques have been developed successfully for various missing-data problems. Collectively, these algorithms provide a comprehensive toolset for the missing-data problem based exclusively on the nonparametric adaptive filter-bank approaches, which are robust and accurate, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.

Author: Wang, Yanwei
Author: Li, Jian
Author: Stoica, Petre
Publisher: Morgan & Claypool Publishers
Illustration: n
Language: ENG
Title: Spectral Analysis of Signals: The Missing Data Case
Pages: 00000 (Encrypted PDF)
On Sale: 2006-01-01
SKU-13/ISBN: 9781598290004
Category: Technology & Engineering : General
Category: Computers : Programming - Algorithms
Category: Technology & Engineering : Signals & Signal Processing


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