Detection Estimation and Modulation Theory, Part I Detection, Estimation, and Filtering Theory.

Detalles Bibliográficos
Autor principal: Van Trees, Harry L.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2013.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Title Page
  • Copyright
  • Dedication
  • Preface
  • Preface to the First Edition
  • Chapter 1: Introduction
  • 1.1 Introduction
  • 1.2 Topical Outline
  • 1.3 Possible Approaches
  • 1.4 Organization
  • Chapter 2: Classical Detection Theory
  • 2.1 Introduction
  • 2.2 Simple Binary Hypothesis Tests
  • 2.3 M Hypotheses
  • 2.4 Performance Bounds and Approximations
  • 2.5 Monte Carlo Simulation
  • 2.6 Summary
  • 2.7 Problems
  • Chapter 3: General Gaussian Detection
  • 3.1 Detection of Gaussian Random Vectors
  • 3.2 Equal Covariance Matrices
  • 3.3 Equal Mean Vectors
  • 3.4 General Gaussian
  • 3.5 M Hypotheses
  • 3.6 Summary
  • 3.7 Problems
  • Chapter 4: Classical Parameter Estimation
  • 4.1 Introduction
  • 4.2 Scalar Parameter Estimation
  • 4.3 Multiple Parameter Estimation
  • 4.4 Global Bayesian Bounds
  • 4.5 Composite Hypotheses
  • 4.6 Summary
  • 4.7 Problems
  • Chapter 5: General Gaussian Estimation
  • 5.1 Introduction
  • 5.2 Nonrandom Parameters
  • 5.3 Random Parameters
  • 5.4 Sequential Estimation
  • 5.5 Summary
  • 5.6 Problems
  • Chapter 6: Representation of Random Processes
  • 6.1 Introduction
  • 6.2 Orthonormal Expansions: Deterministic Signals
  • 6.3 Random Process Characterization
  • 6.4 Homogeous Integral Equations and Eigenfunctions
  • 6.5 Vector Random Processes
  • 6.6 Summary
  • 6.7 Problems
  • Chapter 7: Detection of Signals-Estimation of Signal Parameters
  • 7.1 Introduction
  • 7.2 Detection and Estimation in White Gaussian Noise
  • 7.3 Detection and Estimation in Nonwhite Gaussian Noise
  • 7.4 Signals with Unwanted Parameters: The Composite Hypothesis Problem
  • 7.5 Multiple Channels
  • 7.6 Multiple Parameter Estimation
  • 7.7 Summary
  • 7.8 Problems
  • Chapter 8: Estimation of Continuous-Time Random Processes
  • 8.1 Optimum Linear Processors
  • 8.2 Realizable Linear Filters: Stationary Processes, Infinite Past: Wiener Filters
  • 8.3 Gaussian-Markov Processes: Kalman Filter
  • 8.4 Bayesian Estimation of Non-Gaussian Models
  • 8.5 Summary
  • 8.6 Problems
  • Chapter 9: Estimation of Discrete-Time Random Processes
  • 9.1 Introduction
  • 9.2 Discrete-Time Wiener Filtering
  • 9.3 Discrete-Time Kalman Filter
  • 9.4 Summary
  • 9.5 Problems
  • Chapter 10: Detection of Gaussian Signals
  • 10.1 Introduction
  • 10.2 Detection of Continuous-Time Gaussian Processes
  • 10.3 Detection of Discrete-Time Gaussian Processes
  • 10.4 Summary
  • 10.5 Problems
  • Chapter 11: Epilogue
  • 11.1 Classical Detection and Estimation Theory
  • 11.2 Representation of Random Processes
  • 11.3 Detection of Signals and Estimation of Signal Parameters
  • 11.4 Linear Estimation of Random Processes
  • 11.5 Observations
  • 11.6 Conclusion
  • Appendix A: Probability Distributions and Mathematical Functions
  • Appendix B: Example Index
  • References
  • Index

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