Description
Book SynopsisHarry Van Trees s Detection, Estimation, and Modulation Theory, Part I is one of the great time-tested classics in the field of signal processing. This new edition has been thoroughly revised and expanded, making it again the most comprehensive and up-to-date treatment of the subject.
Table of ContentsPreface 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
