Spectral analysis of signals / by Petre Stoica and Randolph Moses
Material type: TextLanguage: English Publication details: New Delhi. : Prentice Hall, 1998.Description: xxii, 452 p. : ill. ; 25 cmISBN:- 9788120343597
- 515.7222 STO/S
Item type | Current library | Home library | Call number | Status | Date due | Barcode | Item holds | |
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Text Book | Central Library, IIT Bhubaneswar | Central Library, IIT Bhubaneswar | 515.7222 STO/S (Browse shelf(Opens below)) | Available | TB627 | |||
Text Book | Central Library, IIT Bhubaneswar | Central Library, IIT Bhubaneswar | 515.7222 STO/S (Browse shelf(Opens below)) | Available | TB625 | |||
Text Book | Central Library, IIT Bhubaneswar | Central Library, IIT Bhubaneswar | 515.7222 STO/S (Browse shelf(Opens below)) | Available | TB626 |
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515.7222 NAD/S Spectral theory of dynamical systems / | 515.7222 STO/S Spectral analysis of signals / | 515.7222 STO/S Spectral analysis of signals / | 515.7222 STO/S Spectral analysis of signals / | 515.7222 TRE/S Spectral methods in MATLAB / | 515.723 DYK/A An introduction to Laplace transforms and Fourier series | 515.723 FOU/F Fourier and Laplace transforms |
Includes bibliographical references and index
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.
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