Fundamentals of Statistical Signal Processing Practical Algorithm Development

ISBN-10: 013280803X

ISBN-13: 9780132808033

Edition: 2013

Authors: Steven M. Kay
List price: $137.00
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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 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

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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: 2.376
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
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