Kalman Filtering Theory and Practice with MATLAB.

Detalles Bibliográficos
Autor principal: Grewal, Mohinder S.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2014.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Preface to the Fourth Edition
  • Acknowledgements
  • List of Abbreviations
  • Chapter 1 Introduction
  • 1.1 Chapter Focus
  • 1.2 On Kalman Filtering
  • 1.2.1 First of All: What Is a Kalman Filter?
  • 1.2.2 How It Came to Be Called a Filter
  • 1.2.3 Its Mathematical Foundations
  • 1.2.4 What It Is Used for
  • 1.3 On Optimal Estimation Methods
  • 1.3.1 Beginnings of Optimal Estimation Theory
  • 1.3.2 Method of Least Squares
  • 1.3.2.1 The Gramian of the Linear Least-Squares Problem
  • 1.3.2.2 Least-Squares Solution
  • 1.3.2.3 Least Squares in Continuous Time
  • 1.3.2.4 Gramian Matrices and Observability
  • 1.3.3 Mathematical Modeling of Uncertainty
  • 1.3.4 The Wiener-Kolmogorov Filter
  • 1.3.4.1 Wiener-Kolmogorov Filter Development
  • 1.3.5 The Kalman Filter
  • 1.3.5.1 Discovery
  • 1.3.5.2 Introduction of the Kalman Filter
  • 1.3.5.3 Early Applications: The Influence of Stanley F. Schmidt
  • 1.3.5.4 Other Accomplishments of Kalman
  • 1.3.5.5 Impact of Kalman Filtering on Technology
  • 1.3.5.6 Relative Advantages of Kalman and Wiener-Kolmogorov Filtering
  • 1.3.6 Implementation Methods
  • 1.3.6.1 Numerical Stability Problems
  • 1.3.6.2 Early ad hoc Fixes
  • 1.3.6.3 James E. Potter (1937-2005) and Square-Root Filtering
  • 1.3.6.4 Improved Square-Root and UD Filters
  • 1.3.6.5 Matrix Decomposition, Factorization, and Triangularization
  • 1.3.6.6 Generalizations
  • 1.3.7 Nonlinear Approximations
  • 1.3.7.1 Extended Kalman Filtering (EKF) for Quasilinear Problems
  • 1.3.7.2 Higher Order Approximations
  • 1.3.7.3 Sampling-Based Methods for Nonlinear Estimation
  • 1.3.8 Truly Nonlinear Estimation
  • 1.3.9 The Detection Problem for Surveillance
  • 1.4 Common Notation
  • 1.4.1 ""Dot"" Notation for Derivatives
  • 1.4.2 Standard Symbols for Kalman Filter Variables
  • 1.4.2.1 State Vector Notation for Kalman Filtering
  • 1.4.3 Common Notation for Array Dimensions
  • 1.5 Summary
  • Problems
  • References
  • Chapter 2 Linear Dynamic Systems
  • 2.1 Chapter Focus
  • 2.1.1 The Bigger Picture
  • 2.1.2 Models for Dynamic Systems
  • 2.1.2.1 Differential Equations and State Variables
  • 2.1.2.2 Other Approaches
  • 2.1.3 Main Points to Be Covered
  • 2.2 Deterministic Dynamic System Models
  • 2.2.1 Dynamic Systems Modeled by Differential Equations
  • 2.2.2 Newtonian Models
  • 2.2.2.1 Rigid-Body Translational Mechanics
  • 2.2.2.2 Rigid-Body Rotational Mechanics
  • 2.2.2.3 Nonrigid Body Dynamic Models
  • 2.2.3 State Variables and State Equations for Deterministic Systems
  • 2.2.3.1 Homogeneous and Nonhomogeneous Differential Equations
  • 2.2.3.2 State Variables Represent the Degrees of Freedom of Dynamic Systems
  • 2.2.4 Continuous Time and Discrete Time
  • 2.2.4.1 Shorthand Notation for Discrete-Time Systems
  • 2.2.5 Time-Varying Systems and Time-Invariant Systems
  • 2.3 Continuous Linear Systems and their Solutions

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