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Produktbild: Fundamentals of Statistical Signal Processing, Volume 3

Fundamentals of Statistical Signal Processing, Volume 3

Fr. 178.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

29.11.2017

Verlag

Pearson Education Limited

Seitenzahl

504

Maße (L/B/H)

2.8/17.5/23.1 cm

Gewicht

816 g

Auflage

1

Sprache

Englisch

ISBN

978-0-13-487840-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

29.11.2017

Verlag

Pearson Education Limited

Seitenzahl

504

Maße (L/B/H)

2.8/17.5/23.1 cm

Gewicht

816 g

Auflage

1

Sprache

Englisch

ISBN

978-0-13-487840-9

Herstelleradresse

Pearson Education Limited
The Strand 80
WC2R 0RL London
GB
Email: buchhandel@pearson.de
Url: www.stark-verlag.de
Telephone: +49 811 600040
Fax: +49 811 60004601

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  • Produktbild: Fundamentals of Statistical Signal Processing, Volume 3


  • Preface         xiii

    About the Author         xvii


     



    Part I: Methodology and General Approaches          1





    Chapter 1: Introduction         3


    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


    4D Solutions to Exercises    123


     


    Chapter 5: Signal Model Selection         129


    5.1 Introduction    129


    5.2 Signal Modeling    130


    5.3 An Example    131


    5.4 Estimation of Parameters    136


    5.5 Model Order Selection    138


    5.6 Lessons Learned    142


    References    143


    5A Solutions to Exercises    145


     


    Chapter 6: Noise Model Selection          149


    6.1 Introduction    149


    6.2 Noise Modeling    150


    6.3 An Example    152


    6.4 Estimation of Noise Characteristics     161


    6.5 Model Order Selection    176


    6.6 Lessons Learned    177


    References    178


    6A Confidence Intervals    179


    6B Solutions to Exercises    183


     


    Chapter 7: Performance Evaluation, Testing, and Documentation         189


    7.1 Introduction    189


    7.2 Why Use a Computer Simulation Evaluation?    189


    7.3 Statistically Meaningful Performance Metrics    190


    7.4 Performance Bounds    202


    7.5 Exact versus Asymptotic Performance    204


    7.6 Sensitivity    206


    7.7 Valid Performance Comparisons    207


    7.8 Performance/Complexity Tradeoffs    209


    7.9 Algorithm Software Development    210


    7.10 Algorithm Documentation    214


    7.11 Lessons Learned    215


    References    216


    7A A Checklist of Information to Be Included in Algorithm Description Document   217


    7B Example of Algorithm Description Document    219


    7C Solutions to Exercises    231


     


    Chapter 8: Optimal Approaches Using  the Big Theorems    235


    8.1 Introduction    235


    8.2 The Big Theorems    237


    8.3 Optimal Algorithms for the Linear Model    251


    8.4 Using the Theorems to Derive a New Result    255


    8.5 Practically Optimal Approaches    257


    8.6 Lessons Learned    261


    References    262


    8A Some Insights into Parameter Estimation    263


    8B Solutions to Exercises    267


     



    Part II: Specific Algorithms         271





    Chapter 9: Algorithms for Estimation         273


    9.1 Introduction    273


    9.2 Extracting Signal Information    274


    9.3 Enhancing Signals Corrupted by Noise/Interference    299


    References    308


    9A Solutions to Exercises    311


     


    Chapter 10: Algorithms for Detection          313


    10.1 Introduction    313


    10.2 Signal with Known Form (Known Signal)    315


    10.3 Signal with Unknown Form (Random Signals)    322


    10.4 Signal with Unknown Parameters    326


    References    334


    10A Solutions to Exercises    337


     


    Chapter 11: Spectral Estimation          339


    11.1 Introduction    339


    11.2 Nonparametric (Fourier) Methods    340


    11.3 Parametric (Model-Based) Spectral Analysis    348


    11.4 Time-Varying Power Spectral Densities    356


    References    357


    11A Fourier Spectral Analysis and Filtering    359


    11B The Issue of Zero Padding and Resolution    361


    11C Solutions to Exercises    363


     



    Part III: Real-World Extensions         365





    Chapter 12: Complex Data Extensions         367


    12.1 Introduction    367


    12.2 Complex Signals    371


    12.3 Complex Noise    372


    12.4 Complex Least Squares and the Linear Model    378


    12.5 Algorithm Extensions for Complex Data    379


    12.6 Other Extensions    395


    12.7 Lessons Learned    396


    References    396


    12A Solutions to Exercises    399


     



    Part IV: Real-World Applications         403





    Chapter 13: Case Studies - Estimation Problem         405


    13.1 Introduction    405


    13.2 Estimation Problem - Radar Doppler Center Frequency    406


    13.3 Lessons Learned    416


    References    417


    13A 3 dB Bandwidth of AR PSD    419


    13B Solutions to Exercises    421


     


    Chapter 14: Case Studies - Detection Problem         423


    14.1 Introduction    423


    14.2 Detection Problem—Magnetic Signal Detection    423


    14.3 Lessons Learned    439


    References    439


    14A Solutions to Exercises    441


     


    Chapter 15: Case Studies - Spectral Estimation Problem            443


    15.1 Introduction    443


    15.2 Extracting the Muscle Noise    446


    15.3 Spectral Analysis of Muscle Noise    449


    15.4 Enhancing the ECG Waveform    451


    15.5 Lessons Learned    453


    References    453


    15A Solutions to Exercises    455


     


    Appendix A: Glossary of Symbols and Abbreviations          457


    A.1 Symbols    457


    A.2 Abbreviations    459


     


    Appendix B: Brief Introduction to MATLAB         461


    B.1 Overview of MATLAB   461


    B.2 Plotting in MATLAB    464


     


    Appendix C: Description of CD Contents          467

    [Contents of the CD are available for download for readers of the paperback edition.]




    C.1 CD Folders    467


    C.2 Utility Files Description    467


     



    Index          471