This practical, hands-on book is designed to help scientists, engineers, and students gain deeper expertise and more reliable intuition into the effective practice of statistical signal processing. The third volume in Dr. Steven Kay's internationally respected series, this book brings his earlier coverage of theory into focus by applying it to today's practical projects of interest. It provides a complete and practical methodology for solving signal processing problems and design signal processing systems, as well as extensive features for hands on practice -- including block diagrams, MATLAB programs, illustrations, exercises, case studies, and more. Drawing on more than 35 years of signal processing experience, Dr. Kay guides readers in: * Mastering the mathematical modeling, computer simulation, and performance evaluation techniques needed to develop effective signal processing algorithms * Assimilating and practicing common tools, including useful analytical results and MATLAB implementations for evaluation and testing * Identifying the specific approaches and algorithms that work best in practice: those that have stood the test of time * Successfully pplying the book's methods to real-world problems * Discovering extensions often required in practice * Translating math into code, in stages ranging from simple to complex * Verifying code integrity through test cases
was born in Newark, NJ, on April 5, 1951. He received the B.E. degree from Stevens Institute of Technology, Hoboken, NJ in 1972, the M.S. degree from Columbia University, New York, NY, in 1973, and the Ph.D. degree from Georgia Institute of Technology, Atlanta, GA, in 1980, all in electrical engineering.
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.
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
Part 4. Some Extensions Required in Practice
11. Continuous Signals
12. Multivariate or Multichannel
13. Two-Dimensional Signals
14. Complex Signals