Fundamentals of Statistical Signal Processing, Volume II: Detection Theory

[Steven M. Kay] ☆ Fundamentals of Statistical Signal Processing, Volume II: Detection Theory ½ Read Online eBook or Kindle ePUB. Fundamentals of Statistical Signal Processing, Volume II: Detection Theory Next, review Gaussian, Chi-Squared, F, Rayleigh, and Rician PDFs, quadratic forms of Gaussian random variables, asymptotic Gaussian PDFs, and Monte Carlo Performance Evaluations. 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. Kays Fundamentals of Statistical Signal Processing, Vol. Three chapters introduce the basics of detection based on simple hypothesis testing, i

Fundamentals of Statistical Signal Processing, Volume II: Detection Theory

Author :
Rating : 4.47 (903 Votes)
Asin : 013504135X
Format Type : paperback
Number of Pages : 672 Pages
Publish Date : 2017-05-24
Language : English

DESCRIPTION:

. KAY is Professor of Electrical Engineering at the University of Rhode Island and a leading expert in signal processing. STEVEN M

They range from simple applications of the theory to extensions of the basic concepts. for all computer-generated results. This second volume, entitled Fundamentals of Statistical Signal Processing: Detection Theory, is the application of statistical hypothesis testing to the detection of signals in noise. The first volume, Fundamentals of Statistical Signal Processing: Estimation Theory, was published in 1993 by Prentice-Hall, Inc. Henceforth, it will be referred to as Kay-I 1993. Woodsum of Sonetech, Bedford, New Hampshire, and by D. Thanks are due to J. We have made extensive use of the MATLAB scientific programming language (Version 4.2b) Footnote: MATLAB is a registered trademark of The MathWorks, Inc. Lang of Sanders, a Lockheed-Martin Co., Nashua,

Next, review Gaussian, Chi-Squared, F, Rayleigh, and Rician PDFs, quadratic forms of Gaussian random variables, asymptotic Gaussian PDFs, and Monte Carlo Performance Evaluations. 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. Kay's Fundamentals of Statistical Signal Processing, Vol. 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. This is a thorough, up-to-date introduction to optimizing detection algorithms for implementation on digital computers. 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

"Textbook Binding" is rather poor After buying a book for about $80 I expect to get value for my money. I was extremely disappointed to note that just one week after using the textbook (and solving exactly one homework assignment of "Textbook Binding" is rather poor Arun V. Subramanian After buying a book for about $80 I expect to get value for my money. I was extremely disappointed to note that just one week after using the textbook (and solving exactly one homework assignment of 4 problems) the pages began to come out. I have seen other friends who bought the book have a similar problem. T. problems) the pages began to come out. I have seen other friends who bought the book have a similar problem. T. "If you want to learn detection theory get this book." according to Christopher P. Carbone. This book presents the fundamental ideas in detection theory (Hypothesis testing for you math types) in a very accessible way. It was one of the few textbooks I could read without feeling like I should be taking a class to go along with it. First the book starts off with a short chapter explaining what detecti. The best textbook in Estimation Theory Marco Antonio Pulido Steven Kay has done a superb job. His coverage of different aspects of estimation theory make this book an excellent reference for the working engineer, as well as a great college textbook. All the topics are well covered, there are meaningful examples that apply the theory to different aspects of signal proce