Fundamentals of Statistical Signal Processing, Volume 3 (Paperback)

Steven Kay

ISBN 10: 013487840X / ISBN 13: 9780134878409

Published by Pearson Education (US), Upper Saddle River, 2018

About this Item

Paperback. In Fundamentals of Statistical Signal Processing, Volume III: Practical Algorithm Development, author Steven M. Kay shows how to convert theories of statistical signal processing estimation and detection into software algorithms that can be implemented on digital computers. This final volume of Kays three-volume guide builds on the comprehensive theoretical coverage in the first two volumes. Here, Kay helps readers develop strong intuition and expertise in designing well-performing algorithms that solve real-world problems.Kay begins by reviewing methodologies for developing signal processing algorithms, including mathematical modeling, computer simulation, and performance evaluation. He links concepts to practice by presenting useful analytical results and implementations for design, evaluation, and testing. Next, he highlights specific algorithms that have stood the test of time, offers realistic examples from several key application areas, and introduces useful extensions. Finally, he guides readers through translating mathematical algorithms into MATLAB code and verifying solutions. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9780134878409

Bibliographic Details

• Title: Fundamentals of Statistical Signal Processing, Volume 3 (Paperback)
• Publisher: Pearson Education (US), Upper Saddle River
• Publication Date: 2018
• Language: English
• ISBN 10: 013487840X
• ISBN 13: 9780134878409
• Binding: Paperback
• Condition: new

About this title

Synopsis

The Complete, Modern Guide to Developing Well-Performing Signal Processing Algorithms

 

In Fundamentals of Statistical Signal Processing, Volume III: Practical Algorithm Development, author Steven M. Kay shows how to convert theories of statistical signal processing estimation and detection into software algorithms that can be implemented on digital computers. This final volume of Kay’s three-volume guide builds on the comprehensive theoretical coverage in the first two volumes. Here, Kay helps readers develop strong intuition and expertise in designing well-performing algorithms that solve real-world problems.

 

Kay begins by reviewing methodologies for developing signal processing algorithms, including mathematical modeling, computer simulation, and performance evaluation. He links concepts to practice by presenting useful analytical results and implementations for design, evaluation, and testing. Next, he highlights specific algorithms that have “stood the test of time,” offers realistic examples from several key application areas, and introduces useful extensions. Finally, he guides readers through translating mathematical algorithms into MATLAB^® code and verifying solutions.

 

Topics covered include

• Step-by-step approach to the design of algorithms
• Comparing and choosing signal and noise models
• Performance evaluation, metrics, tradeoffs, testing, and documentation
• Optimal approaches using the “big theorems”
• Algorithms for estimation, detection, and spectral estimation
• Complete case studies: Radar Doppler center frequency estimation, magnetic signal detection, and heart rate monitoring

Exercises are presented throughout, with full solutions, and executable MATLAB code that implements all the algorithms is available for download.

 

This new volume is invaluable to engineers, scientists, and advanced students in every discipline that relies on signal processing; researchers will especially appreciate its timely overview of the state of the practical art. Volume III complements Dr. Kay’s Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (Prentice Hall, 1993; ISBN-13: 978-0-13-345711-7), and Volume II: Detection Theory (Prentice Hall, 1998; ISBN-13: 978-0-13-504135-2).

About the Author

Steven M. Kay is one of the world’s leading experts in statistical signal processing. Currently Professor of Electrical Engineering at the University of Rhode Island, Kingston, he has consulted for numerous industrial concerns, the Air Force, Army, and Navy, and has taught short courses to scientists and engineers at NASA and the CIA. Dr. Kay is a Fellow of the IEEE, and a member of Tau Beta Pi, and Sigma Xi and Phi Kappa Phi. He has received the Education Award for “outstanding contributions in education and in writing scholarly book and texts…” from the IEEE Signal Processing society and has been listed as among the 250 most cited researchers in the world in engineering.