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Fundamentals of Statistical Signal Processing: Estimation Theory Steven M. Kay University of Rhode Island pdf

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.
Buy: Fundamentals of Statistical Signal Processing, Volume III (Paperback): Practical Algorithm Development (Prentice-Hall Signal Processing Series) Kindle Edition by Steven M. Kay (Author)

PDF: Fundamentals of Statistical Signal Processing: Estimation Theory Steven M. Kay University of Rhode Island

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