Produktbild: Detection Estimation and Modulation Theory
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Detection Estimation and Modulation Theory Part I

Aus der Reihe Wiley Classics Library

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

22.04.2013

Abbildungen

w. Illustrationen

Verlag

John Wiley & Sons

Seitenzahl

1184

Maße (L/B/H)

25.9/18.6/6 cm

Gewicht

2153 g

Auflage

2nd Edition, Part I edition

Sprache

Englisch

ISBN

978-0-470-54296-5

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

22.04.2013

Abbildungen

w. Illustrationen

Verlag

John Wiley & Sons

Seitenzahl

1184

Maße (L/B/H)

25.9/18.6/6 cm

Gewicht

2153 g

Auflage

2nd Edition, Part I edition

Sprache

Englisch

ISBN

978-0-470-54296-5

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: Detection Estimation and Modulation Theory
  • Preface xv
     
    Preface to the First Edition xix
     
    1 Introduction 1
     
    1.1 Introduction 1
     
    1.2 Topical Outline 1
     
    1.3 Possible Approaches 11
     
    1.4 Organization 14
     
    2 Classical Detection Theory 17
     
    2.1 Introduction 17
     
    2.2 Simple Binary Hypothesis Tests 20
     
    2.3 M Hypotheses 51
     
    2.4 Performance Bounds and Approximations 63
     
    2.5 Monte Carlo Simulation 80
     
    2.6 Summary 109
     
    2.7 Problems 110
     
    3 General Gaussian Detection 125
     
    3.1 Detection of Gaussian Random Vectors 126
     
    3.2 Equal Covariance Matrices 138
     
    3.3 Equal Mean Vectors 174
     
    3.4 General Gaussian 197
     
    3.5 M Hypotheses 209
     
    3.6 Summary 213
     
    3.7 Problems 215
     
    4 Classical Parameter Estimation 230
     
    4.1 Introduction 230
     
    4.2 Scalar Parameter Estimation 232
     
    4.3 Multiple Parameter Estimation 293
     
    4.4 Global Bayesian Bounds 332
     
    4.5 Composite Hypotheses 348
     
    4.6 Summary 375
     
    4.7 Problems 377
     
    5 General Gaussian Estimation 400
     
    5.1 Introduction 400
     
    5.2 Nonrandom Parameters 401
     
    5.3 Random Parameters 483
     
    5.4 Sequential Estimation 495
     
    5.5 Summary 507
     
    5.6 Problems 510
     
    6 Representation of Random Processes 519
     
    6.1 Introduction 519
     
    6.2 Orthonormal Expansions: Deterministic Signals 520
     
    6.3 Random Process Characterization 528
     
    6.4 Homogeous Integral Equations and Eigenfunctions 540
     
    6.5 Vector Random Processes 564
     
    6.6 Summary 568
     
    6.7 Problems 569
     
    7 Detection of Signals-Estimation of Signal Parameters 584
     
    7.1 Introduction 584
     
    7.2 Detection and Estimation in White Gaussian Noise 591
     
    7.3 Detection and Estimation in Nonwhite Gaussian Noise 629
     
    7.4 Signals with Unwanted Parameters: The Composite Hypothesis Problem 675
     
    7.5 Multiple Channels 712
     
    7.6 Multiple Parameter Estimation 716
     
    7.7 Summary 721
     
    7.8 Problems 722
     
    8 Estimation of Continuous-Time Random Processes 771
     
    8.1 Optimum Linear Processors 771
     
    8.2 Realizable Linear Filters: Stationary Processes, Infinite Past: Wiener Filters 787
     
    8.3 Gaussian-Markov Processes: Kalman Filter 807
     
    8.4 Bayesian Estimation of Non-Gaussian Models 842
     
    8.5 Summary 852
     
    8.6 Problems 855
     
    9 Estimation of Discrete-Time Random Processes 880
     
    9.1 Introduction 880
     
    9.2 Discrete-Time Wiener Filtering 882
     
    9.3 Discrete-Time Kalman Filter 919
     
    9.4 Summary 1016
     
    9.5 Problems 1016
     
    10 Detection of Gaussian Signals 1030
     
    10.1 Introduction 1030
     
    10.2 Detection of Continuous-Time Gaussian Processes 1030
     
    10.3 Detection of Discrete-Time Gaussian Processes 1067
     
    10.4 Summary 1076
     
    10.5 Problems 1077
     
    11 Epilogue 1084
     
    11.1 Classical Detection and Estimation Theory 1084
     
    11.2 Representation of Random Processes 1093
     
    11.3 Detection of Signals and Estimation of Signal Parameters 1095
     
    11.4 Linear Estimation of Random Processes 1098
     
    11.5 Observations 1105
     
    11.6 Conclusion 1106
     
    Appendix A: Probability Distributions and Mathematical Functions 1107
     
    Appendix B: Example Index 1119
     
    References 1125
     
    Index 1145