Fundamentals of Statistical Signal Processing Practical Algorithm Development

Name: Fundamentals of Statistical Signal Processing Practical Algorithm Development
Availability: OutOfStock
ISBN: 9780132808033

ISBN-10: 013280803X

ISBN-13: 9780132808033

Edition: 2013

Authors: Steven M. Kay

List price: $137.00

30 day, 100% satisfaction guarantee!

Out of stock

We're sorry. This item is currently unavailable.

what's this?

Rush Rewards U
Members Receive:

You have reached 400 XP and carrot coins. That is the daily max!

Description:

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…

Book details

List price: $137.00
Copyright year: 2013
Publisher: Pearson Education
Publication date: 3/26/2013
Binding: Hardcover
Pages: 496
Size: 7.38" wide x 9.47" long x 1.29" tall
Weight: 2.068
Language: English



Preface


About the Author



Methodology and General Approaches



Introduction



Motivation and Purpose



Core Algorithms



Easy, Hard, and Impossible Problems



Increasing Your Odds for Success-Enhance Your Intuition



Application Areas



Notes to the Reader



Lessons Learned


References



Solutions to Exercises



Methodology for Algorithm Design



Introduction



General Approach



Example of Signal Processing Algorithm Design



Lessons Learned


References



Derivation of Doppler Effect



Solutions to Exercises



Mathematical Modeling of Signals



Introduction



The Hierarchy of Signal Models



Linear vs. Nonlinear Deterministic Signal Models



Deterministic Signals with Known Parameters (Type 1)



Deterministic Signals with Unknown Parameters (Type 2)



Random Signals with Known PDF (Type 3)



Random Signals with PDF Having Unknown Parameters



Lessons Learned


References



Solutions to Exercises



Mathematical Modeling of Noise



Introduction



General Noise Models



White Gaussian Noise



Colored Gaussian Noise



General Gaussian Noise



IID NonGaussian Noise



Randomly Phased Sinusoids



Lessons Learned


References



Random Process Concepts and Formulas



Gaussian Random Processes



Geometrical Interpretation of AR PSD



Solutions to Exercises



Signal Model Selection



Introduction



Signal Modeling



An Example



Estimation of Parameters



Model Order Selection



Lessons Learned


References



Solutions to Exercises



Noise Model Selection



Introduction



Noise Modeling



An Example



Estimation of Noise Characteristics



Model Order Selection



Lessons Learned


References



Confidence Intervals



Solutions to Exercises



Performance Evaluation, Testing, and Documentation



Introduction



Why Use a Computer Simulation Evaluation?



Statistically Meaningful Performance Metrics



Performance Bounds



Exact versus Asymptotic Performance



Sensitivity



Valid Performance Comparisons



Performance/Complexity Tradeoffs



Algorithm Software Development



Algorithm Documentation



Lessons Learned


References



A Checklist of Information to Be Included in Algorithm Description Document



Example of Algorithm Description Document



Solutions to Exercises



Optimal Approaches Using the Big Theorems



Introduction



The Big Theorems



Optimal Algorithms for the Linear Model



Using the Theorems to Derive a New Result



Practically Optimal Approaches



Lessons Learned


References



Some Insights into Parameter Estimation



Solutions to Exercises



Specific Algorithms



Algorithms for Estimation



Introduction



Extracting Signal Information



Enhancing Signals Corrupted by Noise/Interference


References



Solutions to Exercises



Algorithms for Detection



Introduction



Signal with Known Form (Known Signal)



Signal with Unknown Form (Random Signals)



Signal with Unknown Parameters


References



Solutions to Exercises



Spectral Estimation



Introduction



Nonparametric (Fourier) Methods



Parametric (Model-Based) Spectral Analysis



Time-Varying Power Spectral Densities


References



Fourier Spectral Analysis and Filtering



The Issue of Zero Padding and Resolution



Solutions to Exercises



Real-World Extensions



Complex Data Extensions



Introduction



Complex Signals



Complex Noise



Complex Least Squares and the Linear Model



Algorithm Extensions for Complex Data



Other Extensions



Lessons Learned


References



Solutions to Exercises



Real-World Applications



Case Studies - Estimation Problem



Introduction



Estimation Problem - Radar Doppler Center Frequency



Lessons Learned


References



3 dB Bandwidth of AR PSD



Solutions to Exercises



Case Studies - Detection Problem



Introduction



Detection Problem - Magnetic Signal Detection



Lessons Learned


References



Solutions to Exercises



Case Studies - Spectral Estimation Problem



Introduction



Extracting the Muscle Noise



Spectral Analysis of Muscle Noise



Enhancing the ECG Waveform



Lessons Learned


References



Solutions to Exercises



Glossary of Symbols and Abbreviations



Symbols



Abbreviations



Brief Introduction to MATLAB



Overview of MATLAB



Plotting in MATLAB



Description of CD Contents



CD Folders



Utility Files Description


Index