Model-based processing : an applied subspace identification approach

Model-based processing an applied subspace identification approach

A bridge between the application of subspace-based methods for parameter estimation in signal processing and subspace-based system identification in control systems Model-Based Processing : An Applied Subspace Identification Approach provides expert insight on developing models for designing model-...

Descripción completa

Detalles Bibliográficos
Otros Autores: Candy, James V., author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, NJ : John Wiley & Sons, Inc 2019.
Edición:1st edition
Colección:THEi Wiley ebooks.
Materias:
Signal processing > Digital techniques > Mathematics.
Automatic control > Mathematical models.
Invariant subspaces.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630608906719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Preface
  • Acknowledgements
  • Glossary
  • Chapter 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
  • Chapter 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
  • 2.4.1 Spectral Theorems
  • 2.4.2 ARMAX Modeling
  • 2.5 Spectral Estimation
  • 2.5.1 Classical (Nonparametric) Spectral Estimation
  • 2.5.1.1 Correlation Method (Blackman-Tukey)
  • 2.5.1.2 Average Periodogram Method (Welch)
  • 2.5.2 Modern (Parametric) Spectral Estimation
  • 2.5.2.1 Autoregressive (All‐Pole) Spectral Estimation
  • 2.5.2.2 Autoregressive Moving Average Spectral Estimation
  • 2.5.2.3 Minimum Variance Distortionless Response (MVDR) Spectral Estimation
  • 2.5.2.4 Multiple Signal Classification (MUSIC) Spectral Estimation
  • 2.6 Case Study: Spectral Estimation of Bandpass Sinusoids
  • 2.7 Summary
  • Matlab Notes
  • References
  • Problems
  • Chapter 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
  • 3.4.1 Linear Discrete Time‐Invariant Systems
  • 3.4.2 Discrete Systems Theory
  • 3.4.3 Equivalent Linear Systems
  • 3.4.4 Stable Linear Systems
  • 3.5 Gauss-Markov State‐Space Models
  • 3.5.1 Discrete‐Time Gauss-Markov Models
  • 3.6 Innovations Model
  • 3.7 State‐Space Model Structures
  • 3.7.1 Time‐Series Models
  • 3.7.2 State‐Space and Time‐Series Equivalence Models
  • 3.8 Nonlinear (Approximate) Gauss-Markov State‐Space Models
  • 3.9 Summary
  • MATLAB Notes
  • References.
  • Chapter 4 Model‐Based Processors
  • 4.1 Introduction
  • 4.2 Linear Model‐Based Processor: Kalman Filter
  • 4.2.1 Innovations Approach
  • 4.2.2 Bayesian Approach
  • 4.2.3 Innovations Sequence
  • 4.2.4 Practical Linear Kalman Filter Design: Performance Analysis
  • 4.2.5 Steady‐State Kalman Filter
  • 4.2.6 Kalman Filter/Wiener Filter Equivalence
  • 4.3 Nonlinear State‐Space Model‐Based Processors
  • 4.3.1 Nonlinear Model‐Based Processor: Linearized Kalman Filter
  • 4.3.2 Nonlinear Model‐Based Processor: Extended Kalman Filter
  • 4.3.3 Nonlinear Model‐Based Processor: Iterated-Extended Kalman Filter
  • 4.3.4 Nonlinear Model‐Based Processor: Unscented Kalman Filter
  • 4.3.5 Practical Nonlinear Model‐Based Processor Design: Performance Analysis
  • 4.3.6 Nonlinear Model‐Based Processor: Particle Filter
  • 4.3.7 Practical Bayesian Model‐Based Design: Performance Analysis
  • 4.4 Case Study: 2D‐Tracking Problem
  • 4.5 Summary
  • MATLAB Notes
  • References
  • Problems
  • Chapter 5 Parametrically Adaptive Processors
  • 5.1 Introduction
  • 5.2 Parametrically Adaptive Processors: Bayesian Approach
  • 5.3 Parametrically Adaptive Processors: Nonlinear Kalman Filters
  • 5.3.1 Parametric Models
  • 5.3.2 Classical Joint State/Parametric Processors: Augmented Extended Kalman Filter
  • 5.3.3 Modern Joint State/Parametric Processor: Augmented Unscented Kalman Filter
  • 5.4 Parametrically Adaptive Processors: Particle Filter
  • 5.4.1 Joint State/Parameter Estimation: Particle Filter
  • 5.5 Parametrically Adaptive Processors: Linear Kalman Filter
  • 5.6 Case Study: Random Target Tracking
  • 5.7 Summary
  • MATLAB Notes
  • References
  • Problems
  • Chapter 6 Deterministic Subspace Identification
  • 6.1 Introduction
  • 6.2 Deterministic Realization Problem
  • 6.2.1 Realization Theory
  • 6.2.2 Balanced Realizations
  • 6.2.3 Systems Theory Summary
  • 6.3 Classical Realization.
  • 6.3.1 Ho-Kalman Realization Algorithm
  • 6.3.2 SVD Realization Algorithm
  • 6.3.2.1 Realization: Linear Time‐Invariant Mechanical Systems
  • 6.3.3 Canonical Realization
  • 6.3.3.1 Invariant System Descriptions
  • 6.3.3.2 Canonical Realization Algorithm
  • 6.4 Deterministic Subspace Realization: Orthogonal Projections
  • 6.4.1 Subspace Realization: Orthogonal Projections
  • 6.4.2 Multivariable Output Error State‐Space (MOESP) Algorithm
  • 6.5 Deterministic Subspace Realization: Oblique Projections
  • 6.5.1 Subspace Realization: Oblique Projections
  • 6.5.2 Numerical Algorithms for Subspace State‐Space System Identification (N4SID) Algorithm
  • 6.6 Model Order Estimation and Validation
  • 6.6.1 Order Estimation: SVD Approach
  • 6.6.2 Model Validation
  • 6.7 Case Study: Structural Vibration Response
  • 6.8 Summary
  • MATLAB Notes
  • References
  • Problems
  • Chapter 7 Stochastic Subspace Identification
  • 7.1 Introduction
  • 7.2 Stochastic Realization Problem
  • 7.2.1 Correlated Gauss-Markov Model
  • 7.2.2 Gauss-Markov Power Spectrum
  • 7.2.3 Gauss-Markov Measurement Covariance
  • 7.2.4 Stochastic Realization Theory
  • 7.3 Classical Stochastic Realization via the Riccati Equation
  • 7.4 Classical Stochastic Realization via Kalman Filter
  • 7.4.1 Innovations Model
  • 7.4.2 Innovations Power Spectrum
  • 7.4.3 Innovations Measurement Covariance
  • 7.4.4 Stochastic Realization: Innovations Model
  • 7.5 Stochastic Subspace Realization: Orthogonal Projections
  • 7.5.1 Multivariable Output Error State‐SPace (MOESP) Algorithm
  • 7.6 Stochastic Subspace Realization: Oblique Projections
  • 7.6.1 Numerical Algorithms for Subspace State‐Space System Identification (N4SID) Algorithm
  • 7.6.2 Relationship: Oblique (N4SID) and Orthogonal (MOESP) Algorithms
  • 7.7 Model Order Estimation and Validation
  • 7.7.1 Order Estimation: Stochastic Realization Problem.
  • 7.7.1.1 Order Estimation: Statistical Methods
  • 7.7.2 Model Validation
  • 7.7.2.1 Residual Testing
  • 7.8 Case Study: Vibration Response of a Cylinder: Identification and Tracking
  • 7.9 Summary
  • MATLAB NOTES
  • References
  • Problems
  • Chapter 8 Subspace Processors for Physics‐Based Application
  • 8.1 Subspace Identification of a Structural Device
  • 8.1.1 State‐Space Vibrational Systems
  • 8.1.1.1 State‐Space Realization
  • 8.1.2 Deterministic State‐Space Realizations
  • 8.1.2.1 Subspace Approach
  • 8.1.3 Vibrational System Processing
  • 8.1.4 Application: Vibrating Structural Device
  • 8.1.5 Summary
  • 8.2 MBID for Scintillator System Characterization
  • 8.2.1 Scintillation Pulse Shape Model
  • 8.2.2 Scintillator State‐Space Model
  • 8.2.3 Scintillator Sampled‐Data State‐Space Model
  • 8.2.4 Gauss-Markov State‐Space Model
  • 8.2.5 Identification of the Scintillator Pulse Shape Model
  • 8.2.6 Kalman Filter Design: Scintillation/Photomultiplier System
  • 8.2.6.1 Kalman Filter Design: Scintillation/Photomultiplier Data
  • 8.2.7 Summary
  • 8.3 Parametrically Adaptive Detection of Fission Processes
  • 8.3.1 Fission‐Based Processing Model
  • 8.3.2 Interarrival Distribution
  • 8.3.3 Sequential Detection
  • 8.3.4 Sequential Processor
  • 8.3.5 Sequential Detection for Fission Processes
  • 8.3.6 Bayesian Parameter Estimation
  • 8.3.7 Sequential Bayesian Processor
  • 8.3.8 Particle Filter for Fission Processes
  • 8.3.9 SNM Detection and Estimation: Synthesized Data
  • 8.3.10 Summary
  • 8.4 Parametrically Adaptive Processing for Shallow Ocean Application
  • 8.4.1 State‐Space Propagator
  • 8.4.2 State‐Space Model
  • 8.4.2.1 Augmented State‐Space Models
  • 8.4.3 Processors
  • 8.4.4 Model‐Based Ocean Acoustic Processing
  • 8.4.4.1 Adaptive PF Design: Modal Coefficients
  • 8.4.4.2 Adaptive PF Design: Wavenumbers
  • 8.4.5 Summary.
  • 8.5 MBID for Chirp Signal Extraction
  • 8.5.1 Chirp‐like Signals
  • 8.5.1.1 Linear Chirp
  • 8.5.1.2 Frequency‐Shift Key (FSK) Signal
  • 8.5.2 Model‐Based Identification: Linear Chirp Signals
  • 8.5.2.1 Gauss-Markov State‐Space Model: Linear Chirp
  • 8.5.3 Model‐Based Identification: FSK Signals
  • 8.5.3.1 Gauss-Markov State‐Space Model: FSK Signals
  • 8.5.4 Summary
  • References
  • Problems
  • 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
  • B.3.1 Orthogonal (Perpendicular) Projections
  • B.3.2 Oblique (Parallel) Projections
  • 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
  • EULA.

Ejemplares similares