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Fundamentals of Statistical Processing, Volume I: Estimation Theory de Steven M. Kay

Fundamentals of Statistical Processing, Volume I: Estimation Theory: Prentice Hall Signal Processing Series

Autor Steven M. Kay
en Limba Engleză Hardback – 31 mar 1993
A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. Covers important approaches to obtaining an optimal estimator and analyzing its performance; and includes numerous examples as well as applications to real- world problems. MARKETS: For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals -- radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc.
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Specificații

ISBN-13: 9780133457117
ISBN-10: 0133457117
Pagini: 625
Dimensiuni: 184 x 241 x 28 mm
Greutate: 1.04 kg
Ediția:New.
Editura: Prentice Hall
Seria Prentice Hall Signal Processing Series

Locul publicării:Upper Saddle River, United States

Descriere

For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals — radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc.
A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms.

Cuprins



 1. Introduction.


 2. Minimum Variance Unbiased Estimation.


 3. Cramer-Rao Lower Bound.


 4. Linear Models.


 5. General Minimum Variance Unbiased Estimation.


 6. Best Linear Unbiased Estimators.


 7. Maximum Likelihood Estimation.


 8. Least Squares.


 9. Method of Moments.


10. The Bayesian Philosophy.


11. General Bayesian Estimators.


12. Linear Bayesian Estimators.


13. Kalman Filters.


14. Summary of Estimators.


15. Extension for Complex Data and Parameters.


Appendix: Review of Important Concepts.


Glossary of Symbols and Abbreviations.

Caracteristici

  • describes the field of parameter estimation based on time series data.
  • provides a summary of principal approaches as well as a “roadmap” to use in the selection of an estimator.
  • extends many of the results for real data/real parameters to complex data/complex parameters.
  • summarizes as examples many of the important estimators used in practice.
  • illustrates how a digital computer can be used to assess performance of an estimator.
  • emphasizes a linear model to allow an optimal estimator to be found by inspection of a data model.

Textul de pe ultima copertă

For those involved in the design and implementation of signal processing algorithms, this book strikes a balance between highly theoretical expositions and the more practical treatments, covering only those approaches necessary for obtaining an optimal estimator and analyzing its performance. Author Steven M. Kay discusses classical estimation followed by Bayesian estimation, and illustrates the theory with numerous pedagogical and real-world examples. Special features include over 230 problems designed to reinforce basic concepts and to derive additional results; summary chapter containing an overview of all principal methods and the rationale for choosing a particular one; unified treatment of Wiener and Kalman filtering; estimation approaches for complex data and parameters; and over 100 examples, including real-world applications to high resolution spectral analysis, system identification, digital filter design, adaptive noise cancelation, adaptive beamforming, tracking and localization, and more. Students as well as practicing engineers will find Fundamentals of Statistical Signal Processing an invaluable introduction to parameter estimation theory and a convenient reference for the design of successful parameter estimation algorithms.