Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (v. 1)

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9780133457117: Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory  (v. 1)

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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This text is geared towards a one-semester graduate-level course in statistical signal processing and estimation theory. The author balances technical detail with practical and implementation issues, delivering an exposition that is both theoretically rigorous and application-oriented. The book covers topics such as minimum variance unbiased estimators, the Cramer-Rao bound, best linear unbiased estimators, maximum likelihood estimation, recursive least squares, Bayesian estimation techniques, and the Wiener and Kalman filters. The author provides numerous examples, which illustrate both theory and applications for problems such as high-resolution spectral analysis, system identification, digital filter design, adaptive beamforming and noise cancellation, and tracking and localization. The primary audience will be those involved in the design and implementation of optimal estimation algorithms on digital computers. The text assumes that you have a background in probability and random processes and linear and matrix algebra and exposure to basic signal processing. Students as well as researchers and practicing engineers will find the text an invaluable introduction and resource for scalar and vector parameter estimation theory and a convenient reference for the design of successive parameter estimation algorithms.

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Book Description Pearson Education (US), United States, 1993. Hardback. Book Condition: New. US ed. 234 x 188 mm. Language: English . Brand New Book. 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. Bookseller Inventory # AAB9780133457117

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Book Description Pearson Education (US), United States, 1993. Hardback. Book Condition: New. US ed. 234 x 188 mm. Language: English . Brand New Book. 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. Bookseller Inventory # AAB9780133457117

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Book Description Pearson Education (US). Hardback. Book Condition: new. BRAND NEW, Fundamentals of Statistical Signal Processing: v. 1: Estimation Theory (US ed), Steven M. Kay, 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. Bookseller Inventory # B9780133457117

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Book Description Prentice Hall. Hardcover. Book Condition: New. 0133457117 BRAND NEW W/FAST SHIPPING! This item is: Fundamentals of Statistical Processing, Volume I: Estimation Theory, 1st Ed., 1993, by Kay, Steven M.; FORMAT: Hardcover; ISBN: 9780133457117. Choose Expedited for fastest shipping! Our 98%+ rating proves our commitment! We cannot ship to PO Boxes/APO address. To avoid ordering the wrong item, please check your item's ISBN number!. Bookseller Inventory # P9780133457117

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Book Description Prentice Hall, 1993. Book Condition: New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: 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. Bookseller Inventory # ABE_book_new_0133457117

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