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Model-Based Processing

Author James V. Candy
Release Date: 2019/03/01
ISBN: 9781119457763
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Book Description

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. 

Table of Contents

  1. Cover
  2. Preface
    1. References
  3. Acknowledgements
  4. Glossary
  5. 1 Introduction
    1. 1.1 Background
    2. 1.2 Signal Estimation
    3. 1.3 Model‐Based Processing
    4. 1.4 Model‐Based Identification
    5. 1.5 Subspace Identification
    6. 1.6 Notation and Terminology
    7. 1.7 Summary
    8. MATLAB Notes
    9. References
    10. Problems
  6. 2 Random Signals and Systems
    1. 2.1 Introduction
    2. 2.2 Discrete Random Signals
    3. 2.3 Spectral Representation of Random Signals
    4. 2.4 Discrete Systems with Random Inputs
    5. 2.5 Spectral Estimation
    6. 2.6 Case Study: Spectral Estimation of Bandpass Sinusoids
    7. 2.7 Summary
    8. Matlab Notes
    9. References
    10. Problems
  7. 3 State‐Space Models for Identification
    1. 3.1 Introduction
    2. 3.2 Continuous‐Time State‐Space Models
    3. 3.3 Sampled‐Data State‐Space Models
    4. 3.4 Discrete‐Time State‐Space Models
    5. 3.5 Gauss–Markov State‐Space Models
    6. 3.6 Innovations Model
    7. 3.7 State‐Space Model Structures
    8. 3.8 Nonlinear (Approximate) Gauss–Markov State‐Space Models
    9. 3.9 Summary
    10. MATLAB Notes
    11. References
    12. Problems
  8. 4 Model‐Based Processors
    1. 4.1 Introduction
    2. 4.2 Linear Model‐Based Processor: Kalman Filter
    3. 4.3 Nonlinear State‐Space Model‐Based Processors
    4. 4.4 Case Study: 2D‐Tracking Problem
    5. 4.5 Summary
    6. MATLAB Notes
    7. References
    8. Problems
  9. 5 Parametrically Adaptive Processors
    1. 5.1 Introduction
    2. 5.2 Parametrically Adaptive Processors: Bayesian Approach
    3. 5.3 Parametrically Adaptive Processors: Nonlinear Kalman Filters
    4. 5.4 Parametrically Adaptive Processors: Particle Filter
    5. 5.5 Parametrically Adaptive Processors: Linear Kalman Filter
    6. 5.6 Case Study: Random Target Tracking
    7. 5.7 Summary
    8. MATLAB Notes
    9. References
    10. Problems
  10. 6 Deterministic Subspace Identification
    1. 6.1 Introduction
    2. 6.2 Deterministic Realization Problem
    3. 6.3 Classical Realization
    4. 6.4 Deterministic Subspace Realization: Orthogonal Projections
    5. 6.5 Deterministic Subspace Realization: Oblique Projections
    6. 6.6 Model Order Estimation and Validation
    7. 6.7 Case Study: Structural Vibration Response
    8. 6.8 Summary
    9. MATLAB Notes
    10. References
    11. Problems
  11. 7 Stochastic Subspace Identification
    1. 7.1 Introduction
    2. 7.2 Stochastic Realization Problem
    3. 7.3 Classical Stochastic Realization via the Riccati Equation
    4. 7.4 Classical Stochastic Realization via Kalman Filter
    5. 7.5 Stochastic Subspace Realization: Orthogonal Projections
    6. 7.6 Stochastic Subspace Realization: Oblique Projections
    7. 7.7 Model Order Estimation and Validation
    8. 7.8 Case Study: Vibration Response of a Cylinder: Identification and Tracking
    9. 7.9 Summary
    10. MATLAB NOTES
    11. References
    12. Problems
  12. 8 Subspace Processors for Physics‐Based Application
    1. 8.1 Subspace Identification of a Structural Device
    2. 8.2 MBID for Scintillator System Characterization
    3. 8.3 Parametrically Adaptive Detection of Fission Processes
    4. 8.4 Parametrically Adaptive Processing for Shallow Ocean Application
    5. 8.5 MBID for Chirp Signal Extraction
    6. References
  13. Appendix A: Probability and Statistics Overview
    1. A.1 Probability Theory
    2. A.2 Gaussian Random Vectors
    3. A.3 Uncorrelated Transformation: Gaussian Random Vectors
    4. A.4 Toeplitz Correlation Matrices
    5. A.5 Important Processes
    6. References
  14. Appendix B: Projection Theory
    1. B.1 Projections: Deterministic Spaces
    2. B.2 Projections: Random Spaces
    3. B.3 Projection: Operators
    4. References
  15. Appendix C: Matrix Decompositions
    1. C.1 Singular‐Value Decomposition
    2. C.2 QR‐Decomposition
    3. C.3 LQ‐Decomposition
    4. References
  16. Appendix D: Output‐Only Subspace Identification
    1. References
  17. Index
  18. End User License Agreement
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