Overview

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.

Full Product Details

Author:   Steven Kay
Publisher:   Pearson Education (US)
Imprint:   Pearson
Dimensions:   Width: 17.60cm , Height: 23.20cm , Length: 3.00cm
Weight:   0.780kg
ISBN:  

9780134878409

ISBN 10:   013487840
Pages:   504
Publication Date:   09 August 2018
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In stock  
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Table of Contents

Part I: Methodology and General Approaches Chapter 1: Introduction Chapter 2: Methodology for Algorithm Design Chapter 3: Mathematical Modeling of Signals Chapter 4: Mathematical Modeling of Noise Chapter 5: Signal Model Selection Chapter 6: Noise Model Selection  Chapter 7: Performance Evaluation, Testing, and Documentation Chapter 8: Optimal Approaches Using the Big Theorems Part II: Specific Algorithms Chapter 9: Algorithms for Estimation Chapter 10: Algorithms for Detection Chapter 11: Spectral Estimation Part III: Real-World Extensions Chapter 12: Complex Data Extensions Part IV: Real-World Applications Chapter 13: Case Studies - Estimation Problem Chapter 14: Case Studies - Detection Problem Chapter 15: Case Studies - Spectral Estimation Problem

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Author Information

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.

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