Fundamentals of Statistical Signal Processing, Volume III: Practical Algorithm Development

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 provided on the accompanying CD.

This new volumewill be invaluable to electrical 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).

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 provided on the accompanying CD.

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).

From 1972 to 1975, he was with Bell Laboratories, Holmdel, NJ, where he was involved with transmission planning for speech communications and simulation and subjective testing of speech processing algorithms.

From 1975 to 1977, he attended Georgia Institute of Technology to study communication theory and digital signal processing. From 1977 to 1980, he was with the Submarine Signal Division, Portsmouth, RI, where he engaged in research on autoregressive spectral estimation and the design of sonar systems. He is presently a Professor of Electrical Engineering at the University of Rhode Island, Kingston, and a consultant to numerous industrial concerns, the Air Force, the Army, and the Navy.

As a leading expert in statistical signal processing, he has been invited to teach short courses to scientists and engineers at government laboratories, including NASA and the CIA. He has written numerous journal and conference papers and is a contributor to several edited books. He is the author of the textbooks Modern Spectral Estimation (Prentice-Hall, 1988), Fundamentals of Statistical Signal Processing, Vol. I: Estimation Theory (Prentice-Hall, 1993), Fundamentals of Statistical Signal Processing, Vol. II: Detection Theory (Prentice-Hall, 1998), and Intuitive Probability and Random Processes using MATLAB (Springer, 2005).  His current interests are spectrum analysis, detection and estimation theory, and statistical signal processing.

Dr. Kay is a Fellow of the IEEE, and a member of Tau Beta Pi and Sigma Xi. He has been a distinguished lecturer for the IEEE signal processing society. He has been an associate editor for the IEEE Signal Processing Letters and the IEEE Transactions on Signal Processing. He has received the IEEE signal processing society education award “for outstanding contributions in education and in writing scholarly books and texts...”

Dr. Kay has recently been included on a list of the 250 most cited researchers in the world in engineering.

Preface

Introduction

Part 1. Methodology and General Approaches

1. Overall Approach With Block Diagram Roadmap

2. Mathematical Modeling

3. General Methods, Assumptions, and Performance

4. Testing and Evaluation

Part 2. Specific Problems and Methods

5. Solving Estimation Problems

6. Solving Detection Problems

7. Doing Spectral Analysis

Part 3. Real-World Case Studies

8. Radar/Sonar

9. Biomedical

10. Communications

Part 4. Some Extensions Required in Practice

11. Continuous Signals

12. Multivariate or Multichannel

13. Two-Dimensional Signals

14. Complex Signals

Appendices