Fundamentals of Statistical Signal Processing, Volume III: Practical Algorithm Development, Rough Cuts

Description

This is the Rough Cut version of the printed book.

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, 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. Kay’s 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).

Sample Content

Table of Contents

Preface         xiii

About the Author         xvii

1.1 Motivation and Purpose    3

1.2 Core Algorithms   4

1.3 Easy, Hard, and Impossible Problems    5

1.4 Increasing Your Odds for Success—Enhance Your Intuition    11

1.5 Application Areas    13

1.6 Notes to the Reader    14

1.7 Lessons Learned    15

References   16

1A Solutions to Exercises    19

Chapter 2: Methodology for Algorithm Design         23

2.1 Introduction    23

2.2 General Approach    23

2.3 Example of Signal Processing Algorithm Design    31

2.4 Lessons Learned    47

References    48

2A Derivation of Doppler Effect    49

2B Solutions to Exercises    53

Chapter 3: Mathematical Modeling of Signals         55

3.1 Introduction    55

3.2 The Hierarchy of Signal Models    57

3.3 Linear vs. Nonlinear Deterministic Signal Models    61

3.4 Deterministic Signals with Known Parameters (Type 1)   62

3.5 Deterministic Signals with Unknown Parameters (Type 2)    68

3.6 Random Signals with Known PDF (Type 3)    77

3.7 Random Signals with PDF Having Unknown Parameters    83

3.8 Lessons Learned    83

References    83

3A Solutions to Exercises    85

Chapter 4: Mathematical Modeling of Noise          89

4.1 Introduction    89

4.2 General Noise Models    90

4.3 White Gaussian Noise    93

4.4 Colored Gaussian Noise    94

4.5 General Gaussian Noise    102

4.6 IID NonGaussian Noise    108

4.7 Randomly Phased Sinusoids    113

4.8 Lessons Learned    114

References    115

4A Random Process Concepts and Formulas    117

4B Gaussian Random Processes    119

4C Geometrical Interpretation of AR    121

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