Fundamentals of Statistical Signal Processing: Detection Theory: 002

 
9780135041352: Fundamentals of Statistical Signal Processing: Detection Theory: 002

The most comprehensive overview of signal detection available. This is a thorough, up-to-date introduction to optimizing detection algorithms for implementation on digital computers. It focuses extensively on real-world signal processing applications, including state-of-the-art speech and communications technology as well as traditional sonar/radar systems. Start with a quick review of the fundamental issues associated with mathematical detection, as well as the most important probability density functions and their properties. Next, review Gaussian, Chi-Squared, F, Rayleigh, and Rician PDFs, quadratic forms of Gaussian random variables, asymptotic Gaussian PDFs, and Monte Carlo Performance Evaluations. Three chapters introduce the basics of detection based on simple hypothesis testing, including the Neyman-Pearson Theorem, handling irrelevant data, Bayes Risk, multiple hypothesis testing, and both deterministic and random signals. The author then presents exceptionally detailed coverage of composite hypothesis testing to accommodate unknown signal and noise parameters. These chapters will be especially useful for those building detectors that must work with real, physical data. Other topics covered include: * Detection in nonGaussian noise, including nonGaussian noise characteristics, known deterministic signals, and deterministic signals with unknown parameters * Detection of model changes, including maneuver detection and time-varying PSD detection * Complex extensions, vector generalization, and array processing The book makes extensive use of MATLAB, and program listings are included wherever appropriate. Designed for practicing electrical engineers, researchers, and advanced students, it is an ideal complement to Steven M. Kay's Fundamentals of Statistical Signal Processing, Vol. 1: Estimation Theory (Prentice Hall PTR, 1993, ISBN: 0-13-345711-7).

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Kay, Steven M.
Editore: Lebanon, Indiana, U.S.A.: Prentice Hall (1998)
ISBN 10: 013504135X ISBN 13: 9780135041352
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Descrizione libro Lebanon, Indiana, U.S.A.: Prentice Hall, 1998. Hardcover. Condizione libro: New. Ship out 1 business day,Brand new,US edition, (Express shipping worldwide) 1-4 biz days delivery to worldwide Same shipping fee with US, Canada,Europe country, Australia, item will ship out from either USA or Asia. Codice libro della libreria ABE-5146965115

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Descrizione libro Pearson Education (US), United States, 1998. Hardback. Condizione libro: New. New.. 239 x 180 mm. Language: English . Brand New Book. The most comprehensive overview of signal detection available. This is a thorough, up-to-date introduction to optimizing detection algorithms for implementation on digital computers. It focuses extensively on real-world signal processing applications, including state-of-the-art speech and communications technology as well as traditional sonar/radar systems. Start with a quick review of the fundamental issues associated with mathematical detection, as well as the most important probability density functions and their properties. Next, review Gaussian, Chi-Squared, F, Rayleigh, and Rician PDFs, quadratic forms of Gaussian random variables, asymptotic Gaussian PDFs, and Monte Carlo Performance Evaluations. Three chapters introduce the basics of detection based on simple hypothesis testing, including the Neyman-Pearson Theorem, handling irrelevant data, Bayes Risk, multiple hypothesis testing, and both deterministic and random signals. The author then presents exceptionally detailed coverage of composite hypothesis testing to accommodate unknown signal and noise parameters. These chapters will be especially useful for those building detectors that must work with real, physical data. Other topics covered include: * Detection in nonGaussian noise, including nonGaussian noise characteristics, known deterministic signals, and deterministic signals with unknown parameters * Detection of model changes, including maneuver detection and time-varying PSD detection * Complex extensions, vector generalization, and array processing The book makes extensive use of MATLAB, and program listings are included wherever appropriate. Designed for practicing electrical engineers, researchers, and advanced students, it is an ideal complement to Steven M. Kay s Fundamentals of Statistical Signal Processing, Vol. 1: Estimation Theory (Prentice Hall PTR, 1993, ISBN: 0-13-345711-7). Codice libro della libreria AAB9780135041352

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Descrizione libro 1998. HRD. Condizione libro: New. New Book. Shipped from UK within 10 to 14 business days. Established seller since 2000. Codice libro della libreria PJ-9780135041352

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Descrizione libro Pearson Education (US), United States, 1998. Hardback. Condizione libro: New. New.. 239 x 180 mm. Language: English . Brand New Book. The most comprehensive overview of signal detection available. This is a thorough, up-to-date introduction to optimizing detection algorithms for implementation on digital computers. It focuses extensively on real-world signal processing applications, including state-of-the-art speech and communications technology as well as traditional sonar/radar systems. Start with a quick review of the fundamental issues associated with mathematical detection, as well as the most important probability density functions and their properties. Next, review Gaussian, Chi-Squared, F, Rayleigh, and Rician PDFs, quadratic forms of Gaussian random variables, asymptotic Gaussian PDFs, and Monte Carlo Performance Evaluations. Three chapters introduce the basics of detection based on simple hypothesis testing, including the Neyman-Pearson Theorem, handling irrelevant data, Bayes Risk, multiple hypothesis testing, and both deterministic and random signals. The author then presents exceptionally detailed coverage of composite hypothesis testing to accommodate unknown signal and noise parameters. These chapters will be especially useful for those building detectors that must work with real, physical data. Other topics covered include: * Detection in nonGaussian noise, including nonGaussian noise characteristics, known deterministic signals, and deterministic signals with unknown parameters * Detection of model changes, including maneuver detection and time-varying PSD detection * Complex extensions, vector generalization, and array processing The book makes extensive use of MATLAB, and program listings are included wherever appropriate. Designed for practicing electrical engineers, researchers, and advanced students, it is an ideal complement to Steven M. Kay s Fundamentals of Statistical Signal Processing, Vol. 1: Estimation Theory (Prentice Hall PTR, 1993, ISBN: 0-13-345711-7). Codice libro della libreria AAB9780135041352

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Descrizione libro 1998. HRD. Condizione libro: New. New Book.Shipped from US within 10 to 14 business days. Established seller since 2000. Codice libro della libreria IB-9780135041352

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Descrizione libro Pearson Education, 1998. HRD. Condizione libro: New. New Book. Shipped from US within 10 to 14 business days. Established seller since 2000. Codice libro della libreria IB-9780135041352

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Descrizione libro Prentice Hall. Hardcover. Condizione libro: New. 013504135X BRAND NEW W/FAST SHIPPING! This item is: Fundamentals of Statistical Signal Processing, Volume II: Detection Theory, 1st Ed., 1998, by Kay, Steven M.; FORMAT: Hardcover; ISBN: 9780135041352. 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!. Codice libro della libreria P9780135041352

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Descrizione libro Prentice Hall, 1998. Hardcover. Condizione libro: New. Codice libro della libreria 013504135X

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Descrizione libro Pearson Education (US). Hardback. Condizione libro: new. BRAND NEW, Fundamentals of Statistical Signal Processing: Detection Theory: v.2: Detection Theory, Steven M. Kay, The most comprehensive overview of signal detection available. This is a thorough, up-to-date introduction to optimizing detection algorithms for implementation on digital computers. It focuses extensively on real-world signal processing applications, including state-of-the-art speech and communications technology as well as traditional sonar/radar systems. Start with a quick review of the fundamental issues associated with mathematical detection, as well as the most important probability density functions and their properties. Next, review Gaussian, Chi-Squared, F, Rayleigh, and Rician PDFs, quadratic forms of Gaussian random variables, asymptotic Gaussian PDFs, and Monte Carlo Performance Evaluations. Three chapters introduce the basics of detection based on simple hypothesis testing, including the Neyman-Pearson Theorem, handling irrelevant data, Bayes Risk, multiple hypothesis testing, and both deterministic and random signals. The author then presents exceptionally detailed coverage of composite hypothesis testing to accommodate unknown signal and noise parameters. These chapters will be especially useful for those building detectors that must work with real, physical data. Other topics covered include: * Detection in nonGaussian noise, including nonGaussian noise characteristics, known deterministic signals, and deterministic signals with unknown parameters * Detection of model changes, including maneuver detection and time-varying PSD detection * Complex extensions, vector generalization, and array processing The book makes extensive use of MATLAB, and program listings are included wherever appropriate. Designed for practicing electrical engineers, researchers, and advanced students, it is an ideal complement to Steven M. Kay's Fundamentals of Statistical Signal Processing, Vol. 1: Estimation Theory (Prentice Hall PTR, 1993, ISBN: 0-13-345711-7). Codice libro della libreria B9780135041352

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