Optimal Estimation of Dynamic Systems 2nd Edition by John L Crassidis, John L Junkins ISBN 1439839859 9781439839850

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ISBN 10: 1439839859 
ISBN 13: 9781439839850
Author: John L Crassidis, John L Junkins

Optimal Estimation of Dynamic Systems 2nd Table of contents:

1 Least Squares Approximation
1.1 A Curve Fitting Example
1.2 Linear Batch Estimation
 1.2.1 Linear Least Squares
 1.2.2 Weighted Least Squares
 1.2.3 Constrained Least Squares
1.3 Linear Sequential Estimation
1.4 Nonlinear Least Squares Estimation
1.5 Basis Functions
1.6 Advanced Topics
 1.6.1 Matrix Decompositions in Least Squares
 1.6.2 Kronecker Factorization and Least Squares
 1.6.3 Levenberg-Marquardt Method
 1.6.4 Projections in Least Squares
1.7 Summary
Exercises
References

2 Probability Concepts in Least Squares
2.1 Minimum Variance Estimation
 2.1.1 Estimation without a priori State Estimates
 2.1.2 Estimation with a priori State Estimates
2.2 Unbiased Estimates
2.3 Cramér-Rao Inequality
2.4 Constrained Least Squares Covariance
2.5 Maximum Likelihood Estimation
2.6 Properties of Maximum Likelihood Estimation
 2.6.1 Invariance Principle
 2.6.2 Consistent Estimator
 2.6.3 Asymptotically Gaussian Property
 2.6.4 Asymptotically Efficient Property
2.7 Bayesian Estimation
 2.7.1 MAP Estimation
 2.7.2 Minimum Risk Estimation
2.8 Advanced Topics
 2.8.1 Nonuniqueness of the Weight Matrix
 2.8.2 Analysis of Covariance Errors
 2.8.3 Ridge Estimation
 2.8.4 Total Least Squares
2.9 Summary
Exercises
References

3 Sequential State Estimation
3.1 A Simple First-Order Filter Example
3.2 Full-Order Estimators
 3.2.1 Discrete-Time Estimators
3.3 The Discrete-Time Kalman Filter
 3.3.1 Kalman Filter Derivation
 3.3.2 Stability and Joseph’s Form
 3.3.3 Information Filter and Sequential Processing
 3.3.4 Steady-State Kalman Filter
 3.3.5 Relationship to Least Squares Estimation
 3.3.6 Correlated Measurement and Process Noise
 3.3.7 Cramér-Rao Lower Bound
 3.3.8 Orthogonality Principle
3.4 The Continuous-Time Kalman Filter
 3.4.1 Kalman Filter Derivation in Continuous Time
 3.4.2 Kalman Filter Derivation from Discrete Time
 3.4.3 Stability
 3.4.4 Steady-State Kalman Filter
 3.4.5 Correlated Measurement and Process Noise
3.5 The Continuous-Discrete Kalman Filter
3.6 Extended Kalman Filter
3.7 Unscented Filtering
3.8 Constrained Filtering
3.9 Summary
Exercises
References

4 Advanced Topics in Sequential State Estimation
4.1 Factorization Methods
4.2 Colored-Noise Kalman Filtering
4.3 Consistency of the Kalman Filter
4.4 Consider Kalman Filtering
 4.4.1 Consider Update Equations
 4.4.2 Consider Propagation Equations
4.5 Decentralized Filtering
 4.5.1 Covariance Intersection
4.6 Adaptive Filtering
 4.6.1 Batch Processing for Filter Tuning
 4.6.2 Multiple-Modeling Adaptive Estimation
 4.6.3 Interacting Multiple-Model Estimation
4.7 Ensemble Kalman Filtering
4.8 Nonlinear Stochastic Filtering Theory
 4.8.1 Itô Stochastic Differential Equations
 4.8.2 Itô Formula
 4.8.3 Fokker-Planck Equation
 4.8.4 Kushner Equation
4.9 Gaussian Sum Filtering
4.10 Particle Filtering
 4.10.1 Optimal Importance Density
 4.10.2 Bootstrap Filter
  4.10.2.1 Prediction
  4.10.2.2 Update
  4.10.2.3 Resampling and Roughening
 4.10.3 Rao-Blackwellized Particle Filter
 4.10.4 Navigation Using a Rao-Blackwellized Particle Filter
4.11 Error Analysis
4.12 Robust Filtering
4.13 Summary
Exercises
References

5 Batch State Estimation
5.1 Fixed-Interval Smoothing
 5.1.1 Discrete-Time Formulation
  5.1.1.1 Steady-State Fixed-Interval Smoother
  5.1.1.2 RTS Fixed-Interval Smoother
  5.1.1.3 Stability
 5.1.2 Continuous-Time Formulation
  5.1.2.1 Steady-State Fixed-Interval Smoother
  5.1.2.2 RTS Fixed-Interval Smoother
  5.1.2.3 Stability
 5.1.3 Nonlinear Smoothing
5.2 Fixed-Point Smoothing
 5.2.1 Discrete-Time Formulation
 5.2.2 Continuous-Time Formulation
5.3 Fixed-Lag Smoothing
 5.3.1 Discrete-Time Formulation
 5.3.2 Continuous-Time Formulation
5.4 Advanced Topics
 5.4.1 Estimation/Control Duality
  5.4.1.1 Discrete-Time Formulation
  5.4.1.2 Continuous-Time Formulation
  5.4.1.3 Nonlinear Formulation
 5.4.2 Innovations Process
  5.4.2.1 Discrete-Time Formulation
  5.4.2.2 Continuous-Time Formulation
5.5 Summary
Exercises
References

6 Parameter Estimation: Applications
6.1 Attitude Determination
 6.1.1 Vector Measurement Models
 6.1.2 Maximum Likelihood Estimation
 6.1.3 Optimal Quaternion Solution
 6.1.4 Information Matrix Analysis
6.2 Global Positioning System Navigation
6.3 Simultaneous Localization and Mapping
 6.3.1 3D Point Cloud Registration Using Linear Least Squares
6.4 Orbit Determination
6.5 Aircraft Parameter Identification
6.6 Eigensystem Realization Algorithm
6.7 Summary
Exercises
References

7 Estimation of Dynamic Systems: Applications
7.1 Attitude Estimation
 7.1.1 Multiplicative Quaternion Formulation
 7.1.2 Discrete-Time Attitude Estimation
 7.1.3 Murrell’s Version
 7.1.4 Farrenkopf’s Steady-State Analysis
7.2 Inertial Navigation with GPS
 7.2.1 Extended Kalman Filter Application to GPS/INS
7.3 Orbit Estimation
7.4 Target Tracking of Aircraft
 7.4.1 The α-β Filter
 7.4.2 The α-β-γ Filter
 7.4.3 Aircraft Parameter Estimation
7.5 Smoothing with the Eigensystem Realization Algorithm
7.6 Summary
Exercises
References

8 Optimal Control and Estimation Theory
8.1 Calculus of Variations
8.2 Optimization with Differential Equation Constraints
8.3 Pontryagin’s Optimal Control Necessary Conditions
8.4 Discrete-Time Control
8.5 Linear Regulator Problems
 8.5.1 Continuous-Time Formulation
 8.5.2 Discrete-Time Formulation
8.6 Linear Quadratic-Gaussian Controllers
 8.6.1 Continuous-Time Formulation
 8.6.2 Discrete-Time Formulation
8.7 Loop Transfer Recovery
8.8 Spacecraft Control Design
8.9 Summary

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Tags: John L Crassidis, John L Junkins, Optimal Estimation, Dynamic Systems