Harry L. van Trees, Kristine L. Bell, Zhi Tian

Detection Estimation and Modulation Theory, Part I (eBook, ePUB)

Detection, Estimation, and Filtering Theory

• Format: ePub

Produktbeschreibung

Originally published in 1968, Harry Van Trees's Detection, Estimation, and Modulation Theory, Part I is one of the great time-tested classics in the field of signal processing. Highly readable and practically organized, it is as imperative today for professionals, researchers, and students in optimum signal processing as it was over thirty years ago. The second edition is a thorough revision and expansion almost doubling the size of the first edition and accounting for the new developments thus making it again the most comprehensive and up-to-date treatment of the subject. With a wide range of applications such as radar, sonar, communications, seismology, biomedical engineering, and radar astronomy, among others, the important field of detection and estimation has rarely been given such expert treatment as it is here. Each chapter includes section summaries, realistic examples, and a large number of challenging problems that provide excellent study material. This volume which is Part I of a set of four volumes is the most important and widely used textbook and professional reference in the field.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 1184
• Erscheinungstermin: 5. April 2013
• Englisch
• ISBN-13: 9781118539927
• Artikelnr.: 38248923

Autorenporträt

HARRY L. VAN TREES, ScD., received his BSc. from the United States Military Academy and his ScD. from Massachusetts Institute of Technology. During his fourteen years as a Professor of Electrical Engineering at MIT, he wrote Parts I, II, and III of the DEMT series. On loan from MIT, he served in four senior DoD positions including Chief Scientist of the U.S. Air Force and Principal Deputy Assistant Secretary of Defense (C3I). Returning to academia as an endowed professor at George Mason University, he founded the C3I Center and published Part IV of the DEMT series, Optimum Array Processing. He is currently a University Professor Emeritus. KRISTINE L. BELL, PhD, is a Senior Scientist at Metron, Inc., and an affiliate faculty member in the Statistics Department at George Mason University. She coedited with Dr. Van Trees the Wiley-IEEE book Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking. ZHI TIAN, PhD, is a Professor of Electrical and Computer Engineering at Michigan Technological University. She is a Fellow of the IEEE.

Inhaltsangabe

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