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Optimal Estimation of Dynamic Systems

ISBN-10: 1439839859
ISBN-13: 9781439839850
Edition: 2nd 2011 (Revised)
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Description: This book illustrates the usefulness of estimation in engineering and science. It uses dynamic models to provide immediate results of estimation concepts with minimal reliance on mathematics. This second edition discusses a number of new topics,  More...

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Book details

Edition: 2nd
Copyright year: 2011
Publisher: Taylor & Francis Group
Publication date: 11/18/2011
Binding: Hardcover
Pages: 749
Size: 6.25" wide x 9.25" long x 1.50" tall
Weight: 2.596
Language: English

This book illustrates the usefulness of estimation in engineering and science. It uses dynamic models to provide immediate results of estimation concepts with minimal reliance on mathematics. This second edition discusses a number of new topics, including higher order nonlinear filters, inertial navigation, and nonlinear stochastic processes. The authors cover prototype algorithms to stimulate the development and intelligent use of efficient computer programs. MATLAB#xAE;is used throughout, with the code on a supporting website. In the appendices, the authors review statistics, optimization, probability, and matrix analysis.

Preface
Least Squares Approximation
A Curve Fitting Example
Linear Batch Estimation
Linear Least Squares
Weighted Least Squares
Constrained Least Squares
Linear Sequential Estimation
Nonlinear Least Squares Estimation
Basis Functions
Advanced Topics
Matrix Decompositions in Least Squares
Kronecker Factorization and Least Squares
Levenberg-Marquardt Method
Projections in Least Squares
Summary
Probability Concepts in Least Squares
Minimum Variance Estimation
Estimation without a priori State Estimates
Estimation with a priori State Estimates
Unbiased Estimates
Cramer-Rao Inequality
Constrained Least Squares Covariance
Maximum Likelihood Estimation
Properties of Maximum Likelihood Estimation
Invariance Principle
Consistent Estimator
Asymptotically Gaussian Property
Asymptotically Efficient Property
Bayesian Estimation
MAP Estimation
Minimum Risk Estimation
Advanced Topics
Nonuniqueness of the Weight Matrix
Analysis of Covariance Errors
Ridge Estimation
Total Least Squares
Summary
Sequential State Estimation
A Simple First-Order Filter Example
Full-Order Estimators
Discrete-Time Estimators
The Discrete-Time Kalman Filter
Kalman Filter Derivation
Stability and Joseph's Form
Information Filter and Sequential Processing
Steady-State Kalman Filter
Relationship to Least Squares Estimation
Correlated Measurement and Process Noise
Cram�r-Rao Lower Bound
Orthogonality Principle
The Continuous-Time Kalman Filter
Kalman Filter Derivation in Continuous Time
Kalman Filter Derivation from Discrete Time
Stability
Steady-State Kalman Filter
Correlated Measurement and Process Noise
The Continuous-Discrete Kalman Filter
Extended Kalman Filter
Unscented Filtering
Constrained Filtering
Summary
Advanced Topics in Sequential State Estimation
Factorization Methods
Colored-Noise Kalman Filtering
Consistency of the Kalman Filter
Consider Kalman Filtering
Consider Update Equations
Consider Propagation Equations
Decentralized Filtering
Covariance Intersection
Adaptive Filtering
Batch Processing for Filter Tuning
Multiple-Modeling Adaptive Estimation
Interacting Multiple-Model Estimation
Ensemble Kalman Filtering
Nonlinear Stochastic Filtering Theory
It� Stochastic Differential Equations
It� Formula
Fokker-Planck Equation
Kushner Equation
Gaussian Sum Filtering
Particle Filtering
Optimal Importance Density
Bootstrap Filter
Rao-Blackwellized Particle Filter
Navigation Using a Rao-Blackwellized Particle Filter
Error Analysis
Robust Filtering
Summary
Batch State Estimation
Fixed-Interval Smoothing
Discrete-Time Formulation
Continuous-Time Formulation
Nonlinear Smoothing
Fixed-Point Smoothing
Discrete-Time Formulation
Continuous-Time Formulation
Fixed-Lag Smoothing
Discrete-Time Formulation
Continuous-Time Formulation
Advanced Topics
Estimation/Control Duality
Innovations Process
Summary
Parameter Estimation: Applications
Attitude Determination
Vector Measurement Models
Maximum Likelihood Estimation
Optimal Quaternion Solution
Information Matrix Analysis
Global Positioning System Navigation
Simultaneous Localization and Mapping
3D Point Cloud Registration Using Linear Least Squares
Orbit Determination
Aircraft Parameter Identification
Eigensystem Realization Algorithm
Summary
Estimation of Dynamic Systems: Applications
Attitude Estimation
Multiplicative Quaternion Formulation
Discrete-Time Attitude Estimation
Murrell's Version
Farrenkopf's Steady-State Analysis
Inertial Navigation with GPS
Extended Kalman Filter Application to GPS/INS
Orbit Estimation
Target Tracking of Aircraft
The �-� Filter
The �-�-� Filter
Aircraft Parameter Estimation
Smoothing with the Eigensystem Realization Algorithm
Summary
Optimal Control and Estimation Theory
Calculus of Variations
Optimization with Differential Equation Constraints
Pontryagin's Optimal Control Necessary Conditions
Discrete-Time Control
Linear Regulator Problems
Continuous-Time Formulation
Discrete-Time Formulation
Linear Quadratic-Gaussian Controllers
Continuous-Time Formulation
Discrete-Time Formulation
Loop Transfer Recovery
Spacecraft Control Design
Summary
Review of Dynamic Systems
Linear System Theory
The State-Space Approach
Homogeneous Linear Dynamic Systems
Forced Linear Dynamic Systems
Linear State Variable Transformations
Nonlinear Dynamic Systems
Parametric Differentiation
Observability and Controllability
Discrete-Time Systems
Stability of Linear and Nonlinear Systems
Attitude Kinematics and Rigid Body Dynamics
Attitude Kinematics
Rigid Body Dynamics
Spacecraft Dynamics and Orbital Mechanics
Spacecraft Dynamics
Orbital Mechanics
Inertial Navigation Systems
Coordinate Definitions and Earth Model
GPS Satellites
Simulation of Sensors
INS Equations
Aircraft Flight Dynamics
Vibration
Summary
Matrix Properties
Basic Definitions of Matrices
Vectors
Matrix Norms and Definiteness
Matrix Decompositions
Matrix Calculus
Basic Probability Concepts
Functions of a Single Discrete-Valued Random Variable
Functions of Discrete-Valued Random Variables
Functions of Continuous Random Variables
Stochastic Processes
Gaussian Random Variables
Joint and Conditional Gaussian Case
Probability Inside a Quadratic Hypersurface
Chi-Square Random Variables
Wiener Process
Propagation of Functions through Various Models
Linear Matrix Models
Nonlinear Models
Scalar and Matrix Expectations
Random Sampling from a Covariance Matrix
Parameter Optimization Methods
Unconstrained Extrema
Equality Constrained Extrema
Nonlinear Unconstrained Optimization
Some Geometrical Insights
Methods of Gradients
Second-Order (Gauss-Newton) Algorithm
Computer Software
Index

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