Fundamentals of Statistical Signal Processing, Volume III (Paperback) (häftad)
Format
Häftad (Paperback)
Språk
Engelska
Antal sidor
504
Utgivningsdatum
2017-11-29
Förlag
Pearson
Dimensioner
229 x 175 x 28 mm
Vikt
817 g
Antal komponenter
1
ISBN
9780134878409
Fundamentals of Statistical Signal Processing, Volume III (Paperback) (häftad)

Fundamentals of Statistical Signal Processing, Volume III (Paperback)

Practical Algorithm Development

Häftad Engelska, 2017-11-29
1079
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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 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.

 

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. Kays 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).
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Övrig 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.

Innehållsförteckning

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