Spectral analysis of signals
Subtitle: the missing data case
By: Yanwei Wang, Jian Li, Petre Stoica.
Contributors: Li, Jian, Ph. D., 1965-; Stoica, Petre.
Edition: 1st ed.
Series: Synthesis lectures on signal processing (Online)
Publisher: Morgan & Claypool Publishers
Published: 2005
Place of publication: [San Rafael, Calif.]
Language: eng
Format: Electronic resource
Number of pages: 99
ISBN-13: 9781598290004
Subjects: Signal processing -- Statistical methods; Nonparametric statistics; Missing observations (Statistics); Adaptive signal processing
Description: "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."--Publisher's website.
Notes: Title from PDF t.p. (viewed on Dec. 2, 2005).

Series statement from caption on home page.

Includes bibliographical references (p. 91-96).

Available to subscribers only.

Also available in print.

System requirements: Adobe Acrobat Reader.

Mode of access: World Wide Web.