Detection and Estimation Theory

Detection and Estimation Theory

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For courses in Estimation and Detection Theory offered in departments of Electrical Engineering. This is the first student-friendly textbook to comprehensively address the topics of both detection and estimation - with a thorough discussion of the underlying theory as well as the practical applications. By addressing detection and estimation theory in the same volume, the authors encourage a greater appreciation of the strong coupling and often blurring of these fields of study. In order to modernize classical topics, the text focuses on discrete signal processing with continuous signal presentations included to demonstrate uniformity and consistency of the results.show more

Product details

  • Hardback | 671 pages
  • 180.3 x 236.2 x 30.5mm | 748.44g
  • Pearson Education (US)
  • Pearson
  • Upper Saddle River, NJ, United States
  • English
  • 0130894990
  • 9780130894991

Table of contents

Part I Review Chapters Chapter 1 Review of Probability 1.1 Chapter Highlights 1.2 Definition of Probability 1.3 Conditional Probability 1.4 Bayes' Theorem 1.5 Independent Events 1.6 Random Variables 1.7 Conditional Distributions and Densities 1.8 Functions of One Random Variable 1.9 Moments of a Random Variable 1.10 Distributions with Two Random Variables 1.11 Multiple Random Variables 1.12 Mean-Square Error (MSE) Estimation 1.13 Bibliographical Notes 1.14 Problems Chapter 2 Stochastic Processes 2.1 Chapter Highlights 2.2 Stationary Processes 2.3 Cyclostationary Processes 2.4 Averages and Ergodicity 2.5 Autocorrelation Function 2.6 Power Spectral Density 2.7 Discrete-Time Stochastic Processes 2.8 Spatial Stochastic Processes 2.9 Random Signals 2.10 Bibliographical Notes 2.11 Problems Chapter 3 Signal Representations and Statistics 3.1 Chapter Highlights 3.2 Relationship of Power Spectral Density and Autocorrelation Function 3.3 Sampling Theorem 3.4 Linear Time-Invariant and Linear Shift-Invariant Systems 3.5 Bandpass Signal Representations 3.6 Bibliographical Notes 3.7 Problems Part II Detection Chapters Chapter 4 Single Sample Detection of Binary Hypotheses 4.1 Chapter Highlights 4.2 Hypothesis Testing and the MAP Criterion 4.3 Bayes Criterion 4.4 Minimax Criterion 4.5 Neyman-Pearson Criterion 4.6 Summary of Detection-Criterion Results Used in Chapter 4Examples 4.7 Sequential Detection 4.8 Bibliographical Notes 4.9 Problems Chapter 5 Multiple Sample Detection of Binary Hypotheses 5.1 Chapter Highlights 5.2 Examples of Multiple Measurements 5.3 Bayes Criterion 5.4 Other Criteria 5.5 The Optimum Digital Detector in Additive Gaussian Noise 5.6 Filtering Alternatives 5.7 Continuous Signals-White Gaussian Noise 5.8 Continuous Signals-Colored Gaussian Noise 5.9 Performance of Binary Receivers in AWGN 5.10 Further Receiver-Structure Considerations 5.11 Sequential Detection and Performance 5.12 Bibliographical Notes 5.13 Problems Chapter 6 Detection of Signals with Random Parameters 6.1 Chapter Highlights 6.2 Composite Hypothesis Testing 6.3 Unknown Phase 6.4 Unknown Amplitude 6.5 Unknown Frequency 6.6 Unknown Time of Arrival 6.7 Bibliographical Notes 6.8 Problems Chapter 7 Multiple Pulse Detection with Random Parameters 7.1 Chapter Highlights 7.2 Unknown Phase 7.3 Unknown Phase and Amplitude 7.4 Diversity Approaches and Performances7.5 Unknown Phase, Amplitude, and Frequency 7.6 Bibliographical Notes 7.7 Problems Chapter 8 Detection of Multiple Hypotheses 8.1 Chapter Highlights 8.2 Bayes Criterion 8.3 MAP Criterion 8.4 M-ary Detection Using Other Criteria 8.5 M-ary Decisions with Erasure 8.6 Signal-Space Representations 8.7 Performance of M-ary Detection Systems 8.8 Sequential Detection of Multiple Hypotheses 8.9 Bibliographical Notes 8.10 Problems Chapter 9 Nonparametric Detection 9.1 Chapter Highlights 9.2 Sign Tests 9.3 Wilcoxon Tests 9.4 Other Nonparametric Tests 9.5 Bibliographical Notes 9.6 Problems Part III Estimation Chapters Chapter 10 Fundamentals of Estimation Theory 10.1 Chapter Highlights 10.2 Formulation of the General Parameter Estimation Problem 10.3 Relationship between Detection and Estimation Theory 10.4 Types of Estimation Problems 10.5 Properties of Estimators 10.6 Bayes Estimation 10.7 Minimax Estimation 10.8 Maximum-Likelihood Estimation 10.9 Comparison of Estimators of Parameters 10.10 Bibliographical Notes 10.11 Problems Chapter 11 Estimation of Specific Parameters 11.1 Chapter Highlights 11.2 Parameter Estimation in White Gaussian Noise 11.3 Parameter Estimation in Nonwhite Gaussian Noise 11.4 Amplitude Estimation in the Coherent Case with WGN 11.5 Amplitude Estimation in the Noncoherent Case with WGN 11.6 Phase Estimation in WGN 11.7 Time-Delay Estimation in WGN 11.8 Frequency Estimation in WGN 11.9 Simultaneous Parameter Estimation in WGN 11.10 Whittle Approximation 11.11 Bibliographical Notes 11.12 Problems Chapter 12 Estimation of Multiple Parameters 12.1 Chapter Highlights 12.2 ML Estimation for a Discrete Linear Observation Model 12.3 MAP Estimation for a Discrete Linear Observation Model 12.4 Sequential Parameter Estimation 12.5 Bibliographical References 12.6 Problems Chapter 13 Distribution-Free Estimation-Wiener Filters 13.1 Chapter Highlights 13.2 Orthogonality Principle 13.3 Autoregressive Techniques 13.4 Discrete Wiener Filter 13.5 Continuous Wiener Filter 13.6 Generalization of Discrete and Continuous Filter Representations 13.7 Bibliographical Notes 13.8 Problems Chapter 14 Distribution-Free Estimation-Kalman Filter 14.1 Chapter Highlights 14.2 Linear Least-Squares Methods 14.3 Minimum-Variance Weighted Least-Squares Methods 14.4 Minimum-Variance Least-Squares or Kalman Algorithm 14.5 Kalman Algorithm Computational Considerations 14.6 Kalman Algorithm for Signal Estimation 14.7 Continuous Kalman Filter 14.8 Extended Kalman Filter 14.9 Comments and Extensions 14.10 Bibliographical Notes 14.11 Problems Part IV Application Chapters Chapter 15 Detection and Estimation in Non-Gaussian Noise Systems 15.1 Chapter Highlights 15.2 Characterization of Impulsive Noise 15.3 Detector Structures in Non-Gaussian Noise 15.4 Selected Examples of Noise Models, Receiver Structures, and Error-Rate Performance 15.5 Estimation of Non-Gaussian Noise Parameters 15.6 Bibliographical Notes 15.7 Problems Chapter 16 Direct-Sequence Spread-Spectrum Signals in Fading Multipath Channels 16.1 Chapter Highlights 16.2 Introduction to Direct-Sequence Spread Spectrum Communications 16.3 Fading Multipath Channel Models 16.4 Receiver Structures with Known Channel Parameters 16.5 Receiver Structures without Knowledge of Phase 16.6 Receiver Structures without Knowledge of Amplitude or Phase 16.7 Receiver Structures and Performance with No Channel Knowledge 16.8 Bibliographical Notes 16.9 Problems Chapter 17 Multiuser Detection 17.1 Chapter Highlights 17.2 Introduction 17.3 Synchronous Multiuser Direct-Sequence CDMA 17.4 Asynchronous Multiuser Direct-Sequence CDMA 17.5 Speculative Summary 17.6 Bibliographical Notes 17.7 Problems Chapter 18 Low-Probability-of-Intercept Communications 18.1 Chapter Highlights 18.2 LPI System Model 18.3 Interceptor Detector Structures 18.4 Filter-Bank Combiners 18.5 Feature Detectors 18.6 Bibliographical Notes 18.7 Problems Chapter 19 Spectrum Estimation 19.1 Chapter Highlights 19.2 Overview of Power Spectral Estimation 19.3 Periodogram Techniques 19.4 Parametric Spectral Estimation Techniques 19.5 Examples of Spectral Estimation from MATLAB 19.6 Bibliographical Notes 19.7 Problems Appendix A Properties of Distribution and Density Functions Appendix B Common pdfs, cdfs, and Characteristic Functions B.1 One Point B.2 Zero-One B.3 Binomial B.4 Poisson B.5 Uniform B.6 Exponential B.7 Gaussian-Based Distributions B.8 Compilation of Mean, Variance, and Characteristic Function Appendix C Multiple Normal Random Variables C.1 Zero-Mean Jointly Normal Real Random Variables C.2 Nonzero-Mean Jointly Normal Real Random Variables C.3 Linear Transformation of Zero-Mean Jointly Normal Real RandomVariables C.4 Central Limit Theorem 609C.5 Nonzero Mean Jointly Normal Complex Random Variables Appendix D Properties of Autocorrelation and Power Spectral Density Functions D.1 Autocorrelation Functions-Continuous Processes D.2 Power Spectral Density Functions-Continuous Process D.3 Properties of Discrete Processes Appendix E Equivalence of LTI and LSI Bandlimited Systems Appendix F Theory of Random Sums Appendix G Evaluations Useful for Chapters 6, 7, and 16 Appendix H Gram-Charlier Type Series Appendix I Mobile User Detection I.1 Overview of Commercial Cellular Networks I.2 CDMA I.3 Bibliographical Notes Bibliography Glossary List of Symbols Indexshow more

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