Model-Based Processing

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.

Autorentext

JAMES V. CANDY, PHD, is Chief Scientist for Engineering, Distinguished Member of the Technical Staff, and founder of the Center for Advanced Signal & Image Sciences (CASIS), Lawrence Livermore National Laboratory, Livermore, California. Dr. Candy is also Adjunct Full-Professor, University of California, Santa Barbara, a Fellow of the IEEE, and a Fellow of the Acoustical Society of America. He is author of Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods and Model-Based Signal Processing (John Wiley & Sons, Inc., 2006) and Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods, Second Edition (John Wiley & Sons, Inc., 2016). Dr. Candy was awarded the IEEE Distinguished Technical Achievement Award for his development of model-based signal processing and the Acoustical Society of America Helmholtz-Rayleigh Interdisciplinary Silver Medal for his contributions to acoustical signal processing and underwater acoustics.

Klappentext
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 vehiclesall 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 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.

Inhalt
Preface xiii

Acknowledgements xxi

Glossary xxiii

1 Introduction 1

1.1 Background 1

1.2 Signal Estimation 2

1.3 Model-Based Processing 8

1.4 Model-Based Identification 16

1.5 Subspace Identification 20

1.6 Notation and Terminology 22

1.7 Summary 24

MATLAB Notes 25

References 25

Problems 26

2 Random Signals and Systems 29

2.1 Introduction 29

2.2 Discrete Random Signals 32

2.3 Spectral Representation of Random Signals 36

2.4 Discrete Systems with Random Inputs 40

2.4.1 Spectral Theorems 41

2.4.2 ARMAX Modeling 42

2.5 Spectral Estimation 44

2.5.1 Classical (Nonparametric) Spectral Estimation 44

2.5.1.1 Correlation Method (BlackmanTukey) 45

2.5.1.2 Average Periodogram Method (Welch) 46

2.5.2 Modern (Parametric) Spectral Estimation 47

2.5.2.1 Autoregressive (All-Pole) Spectral Estimation 48

2.5.2.2 Autoregressive Moving Average Spectral Estimation 51

2.5.2.3 Minimum Variance Distortionless Response (MVDR) Spectral Estimation 52

2.5.2.4 Multiple Signal Classification (MUSIC) Spectral Estimation 55

2.6 Case Study: Spectral Estimation of Bandpass Sinusoids 59

2.7 Summary 61

MATLAB Notes 61

References 62

Problems 64

3 State-Space Models for Identification 69

3.1 Introduction 69

3.2 Continuous-Time State-Space Models 69

3.3 Sampled-Data State-Space Models 73

3.4 Discrete-Time State-Space Models 74

3.4.1 Linear Discrete Time-Invariant Systems 77

3.4.2 Discrete Systems Theory 78

3.4.3 Equivalent Linear Systems 82

3.4.4 Stable Linear Systems 83

3.5 GaussMarkov State-Space Models 83

3.5.1 Discrete-Time GaussMarkov Models 83

3.6 Innovations Model 89

3.7 State-Space Model Structures 90

3.7.1 Time-Series Models 91

3.7.2 State-Space and Time-Series Equivalence Models 91

3.8 Nonlinear (Approximate) GaussMarkov State-Space Models 97

3.9 Summary 101

MATLAB Notes 102

References 102

Problems 103

4 Model-Based Processors 107

4.1 Introduc…