Model-based processing : an applied subspace identification approach / James V. Candy.

Format:

Book

Author/Creator:

Candy, James V., author.

Contributor:

IEEE Xplore (Online Service), distributor.

Wiley, publisher.

Series:

THEi Wiley ebooks.

Language:

English

Subjects (All):

Signal processing--Digital techniques--Mathematics.

Signal processing.

Automatic control--Mathematical models.

Automatic control.

Invariant subspaces.

Physical Description:

1 online resource (540 pages)

Edition:

1st edition

Place of Publication:

Hoboken, NJ : John Wiley & Sons, Inc., 2019.

System Details:

Access using campus network via VPN at home (THEi Users Only).

text file

Summary:

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-based signal processors (MBSP) employing subspace identification techniques to achieve model-based identification (MBID) and enables readers to evaluate overall performance using validation and statistical analysis methods. Focusing on subspace approaches to system identification problems, this book teaches readers to identify models quickly and incorporate them into various processing problems including state estimation, tracking, detection, classification, controls, communications, and other applications that require reliable models that can be adapted to dynamic environments. The extraction of a model from data is vital to numerous applications, from the detection of submarines to determining the epicenter of an earthquake to controlling an autonomous vehicles—all requiring a fundamental understanding of their underlying processes and measurement instrumentation. Emphasizing real-world solutions to a variety of model development problems, this text demonstrates how model-based subspace identification system identification enables the extraction of a model from measured data sequences from simple time series polynomials to complex constructs of parametrically adaptive, nonlinear distributed systems. In addition, this resource features: Kalman filtering for linear, linearized, and nonlinear systems; modern unscented Kalman filters; as well as Bayesian particle filters Practical processor designs including comprehensive methods of performance analysis Provides a link between model development and practical applications in model-based signal processing Offers in-depth examination of the subspace approach that applies subspace algorithms to synthesized examples and actual applications Enables readers to bridge the gap from statistical signal processing to subspace identification Includes appendices, problem sets, case studies, examples, and notes for MATLAB Model-Based Processing: An Applied Subspace Identification Approach is essential reading for advanced undergraduate and graduate students of engineering and science as well as engineers working in industry and academia.

Contents:

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

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

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

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

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

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

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

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

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

Appendix C Matrix Decompositions

C.1 Singular‐Value Decomposition

C.2 QR‐Decomposition

C.3 LQ‐Decomposition

Appendix D Output‐Only Subspace Identification

Index

EULA.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781119457770

1119457777

9781119457695

1119457696

OCLC:

1057238048