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by James V. Candy
Model-Based Processing
Cover
Preface
References
Acknowledgements
Glossary
1 Introduction
1.1 Background
1.2 Signal Estimation
1.3 Model‐Based Processing
1.4 Model‐Based Identification
1.5 Subspace Identification
1.6 Notation and Terminology
1.7 Summary
MATLAB Notes
References
Problems
2 Random Signals and Systems
2.1 Introduction
2.2 Discrete Random Signals
2.3 Spectral Representation of Random Signals
2.4 Discrete Systems with Random Inputs
2.5 Spectral Estimation
2.6 Case Study: Spectral Estimation of Bandpass Sinusoids
2.7 Summary
Matlab Notes
References
Problems
3 State‐Space Models for Identification
3.1 Introduction
3.2 Continuous‐Time State‐Space Models
3.3 Sampled‐Data State‐Space Models
3.4 Discrete‐Time State‐Space Models
3.5 Gauss–Markov State‐Space Models
3.6 Innovations Model
3.7 State‐Space Model Structures
3.8 Nonlinear (Approximate) Gauss–Markov State‐Space Models
3.9 Summary
MATLAB Notes
References
Problems
4 Model‐Based Processors
4.1 Introduction
4.2 Linear Model‐Based Processor: Kalman Filter
4.3 Nonlinear State‐Space Model‐Based Processors
4.4 Case Study: 2D‐Tracking Problem
4.5 Summary
MATLAB Notes
References
Problems
5 Parametrically Adaptive Processors
5.1 Introduction
5.2 Parametrically Adaptive Processors: Bayesian Approach
5.3 Parametrically Adaptive Processors: Nonlinear Kalman Filters
5.4 Parametrically Adaptive Processors: Particle Filter
5.5 Parametrically Adaptive Processors: Linear Kalman Filter
5.6 Case Study: Random Target Tracking
5.7 Summary
MATLAB Notes
References
Problems
6 Deterministic Subspace Identification
6.1 Introduction
6.2 Deterministic Realization Problem
6.3 Classical Realization
6.4 Deterministic Subspace Realization: Orthogonal Projections
6.5 Deterministic Subspace Realization: Oblique Projections
6.6 Model Order Estimation and Validation
6.7 Case Study: Structural Vibration Response
6.8 Summary
MATLAB Notes
References
Problems
7 Stochastic Subspace Identification
7.1 Introduction
7.2 Stochastic Realization Problem
7.3 Classical Stochastic Realization via the Riccati Equation
7.4 Classical Stochastic Realization via Kalman Filter
7.5 Stochastic Subspace Realization: Orthogonal Projections
7.6 Stochastic Subspace Realization: Oblique Projections
7.7 Model Order Estimation and Validation
7.8 Case Study: Vibration Response of a Cylinder: Identification and Tracking
7.9 Summary
MATLAB NOTES
References
Problems
8 Subspace Processors for Physics‐Based Application
8.1 Subspace Identification of a Structural Device
8.2 MBID for Scintillator System Characterization
8.3 Parametrically Adaptive Detection of Fission Processes
8.4 Parametrically Adaptive Processing for Shallow Ocean Application
8.5 MBID for Chirp Signal Extraction
References
Appendix A: Probability and Statistics Overview
A.1 Probability Theory
A.2 Gaussian Random Vectors
A.3 Uncorrelated Transformation: Gaussian Random Vectors
A.4 Toeplitz Correlation Matrices
A.5 Important Processes
References
Appendix B: Projection Theory
B.1 Projections: Deterministic Spaces
B.2 Projections: Random Spaces
B.3 Projection: Operators
References
Appendix C: Matrix Decompositions
C.1 Singular‐Value Decomposition
C.2 QR‐Decomposition
C.3 LQ‐Decomposition
References
Appendix D: Output‐Only Subspace Identification
References
Index
End User License Agreement
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