Spectral analysis of signals the missing data case /

• Main Author: Wang, Yanwei, 1973-
• Other Authors: Li, Jian, Ph. D., 1965-, Stoica, Petre.
• Format: Electronic
• Language: English
• Published: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, c2005.
• Edition: 1st ed.
• Series: Synthesis lectures on signal processing (Online), #1.
• Subjects: Adaptive signal processing.; Missing observations (Statistics); Nonparametric statistics.; Signal processing > Statistical methods.
• Online Access: Abstract with links to full text
• Item Description: Part of: Synthesis digital library of engineering and computer science.; Title from PDF t.p. (viewed Oct. 19, 2008).; Series from website.
• Physical Description: 1 electronic text (viii, 99 p. : ill. (some col.)) : digital file.; Also available in print.
• Format: Mode of access: World Wide Web.; System requirements: Adobe Acrobat Reader.
• Bibliography: Includes bibliographical references (p. 91-96).
• ISBN: 1598290002 (electronic bk.); 9781598290004 (electronic bk.)
• ISSN: 1932-1694
• Access: Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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 lecture, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.