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Model-based processing : an applied subspace identification approach / James V. Candy, Lawrence Livermore National Laboratory, University of California Santa Barbara.

By: Candy, James V [author.].
Contributor(s): IEEE Xplore (Online Service) [distributor.] | Wiley [publisher.].
Material type: materialTypeLabelBookPublisher: Hoboken, New Jersey : John Wiley & Sons, Inc., 2019Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2019]Description: 1 PDF (xxv, 511 pages).Content type: text Media type: electronic Carrier type: online resourceISBN: 9781119457695.Subject(s): Signal processing -- Digital techniques -- Mathematics | Automatic control -- Mathematical models | Invariant subspacesGenre/Form: Electronic books.Additional physical formats: Print version:: Model-based processingDDC classification: 621.382/23 Online resources: Abstract with links to resource Also available in print.
Contents:
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 (Blackman-Tukey) 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 Gauss-Markov State-Space Models 83 -- 3.5.1 Discrete-Time Gauss-Markov 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) Gauss-Markov State-Space Models 97 -- 3.9 Summary 101 -- MATLAB Notes 102 -- References 102 -- Problems 103 -- 4 Model-Based Processors 107.
4.1 Introduction 107 -- 4.2 Linear Model-Based Processor: Kalman Filter 108 -- 4.2.1 Innovations Approach 110 -- 4.2.2 Bayesian Approach 114 -- 4.2.3 Innovations Sequence 116 -- 4.2.4 Practical Linear Kalman Filter Design: Performance Analysis 117 -- 4.2.5 Steady-State Kalman Filter 125 -- 4.2.6 Kalman Filter/Wiener Filter Equivalence 128 -- 4.3 Nonlinear State-Space Model-Based Processors 129 -- 4.3.1 Nonlinear Model-Based Processor: Linearized Kalman Filter 130 -- 4.3.2 Nonlinear Model-Based Processor: Extended Kalman Filter 133 -- 4.3.3 Nonlinear Model-Based Processor: Iterated-Extended Kalman Filter 138 -- 4.3.4 Nonlinear Model-Based Processor: Unscented Kalman Filter 141 -- 4.3.5 Practical Nonlinear Model-Based Processor Design: Performance Analysis 148 -- 4.3.6 Nonlinear Model-Based Processor: Particle Filter 151 -- 4.3.7 Practical Bayesian Model-Based Design: Performance Analysis 160 -- 4.4 Case Study: 2D-Tracking Problem 166 -- 4.5 Summary 173 -- MATLAB Notes 173 -- References 174 -- Problems 177 -- 5 Parametrically Adaptive Processors 185 -- 5.1 Introduction 185 -- 5.2 Parametrically Adaptive Processors: Bayesian Approach 186 -- 5.3 Parametrically Adaptive Processors: Nonlinear Kalman Filters 187 -- 5.3.1 Parametric Models 188 -- 5.3.2 Classical Joint State/Parametric Processors: Augmented Extended Kalman Filter 190 -- 5.3.3 Modern Joint State/Parametric Processor: Augmented Unscented Kalman Filter 198 -- 5.4 Parametrically Adaptive Processors: Particle Filter 201 -- 5.4.1 Joint State/Parameter Estimation: Particle Filter 201 -- 5.5 Parametrically Adaptive Processors: Linear Kalman Filter 208 -- 5.6 Case Study: Random Target Tracking 214 -- 5.7 Summary 222 -- MATLAB Notes 223 -- References 223 -- Problems 226 -- 6 Deterministic Subspace Identification 231 -- 6.1 Introduction 231 -- 6.2 Deterministic Realization Problem 232 -- 6.2.1 Realization Theory 233 -- 6.2.2 Balanced Realizations 238 -- 6.2.3 Systems Theory Summary 239 -- 6.3 Classical Realization 241.
6.3.1 Ho-Kalman Realization Algorithm 241 -- 6.3.2 SVD Realization Algorithm 243 -- 6.3.2.1 Realization: Linear Time-Invariant Mechanical Systems 246 -- 6.3.3 Canonical Realization 251 -- 6.3.3.1 Invariant System Descriptions 251 -- 6.3.3.2 Canonical Realization Algorithm 257 -- 6.4 Deterministic Subspace Realization: Orthogonal Projections 264 -- 6.4.1 Subspace Realization: Orthogonal Projections 266 -- 6.4.2 Multivariable Output Error State-Space (MOESP) Algorithm 271 -- 6.5 Deterministic Subspace Realization: Oblique Projections 274 -- 6.5.1 Subspace Realization: Oblique Projections 278 -- 6.5.2 Numerical Algorithms for Subspace State-Space System Identification (N4SID) Algorithm 280 -- 6.6 Model Order Estimation and Validation 285 -- 6.6.1 Order Estimation: SVD Approach 286 -- 6.6.2 Model Validation 289 -- 6.7 Case Study: Structural Vibration Response 295 -- 6.8 Summary 299 -- MATLAB Notes 300 -- References 300 -- Problems 303 -- 7 Stochastic Subspace Identification 309 -- 7.1 Introduction 309 -- 7.2 Stochastic Realization Problem 312 -- 7.2.1 Correlated Gauss-Markov Model 312 -- 7.2.2 Gauss-Markov Power Spectrum 313 -- 7.2.3 Gauss-Markov Measurement Covariance 314 -- 7.2.4 Stochastic Realization Theory 315 -- 7.3 Classical Stochastic Realization via the Riccati Equation 317 -- 7.4 Classical Stochastic Realization via Kalman Filter 321 -- 7.4.1 Innovations Model 321 -- 7.4.2 Innovations Power Spectrum 322 -- 7.4.3 Innovations Measurement Covariance 323 -- 7.4.4 Stochastic Realization: Innovations Model 325 -- 7.5 Stochastic Subspace Realization: Orthogonal Projections 330 -- 7.5.1 Multivariable Output Error State-SPace (MOESP) Algorithm 334 -- 7.6 Stochastic Subspace Realization: Oblique Projections 342 -- 7.6.1 Numerical Algorithms for Subspace State-Space System Identification (N4SID) Algorithm 346 -- 7.6.2 Relationship: Oblique (N4SID) and Orthogonal (MOESP) Algorithms 351 -- 7.7 Model Order Estimation and Validation 353 -- 7.7.1 Order Estimation: Stochastic Realization Problem 354.
7.7.1.1 Order Estimation: Statistical Methods 356 -- 7.7.2 Model Validation 362 -- 7.7.2.1 Residual Testing 363 -- 7.8 Case Study: Vibration Response of a Cylinder: Identification and Tracking 369 -- 7.9 Summary 378 -- MATLAB NOTES 378 -- References 379 -- Problems 382 -- 8 Subspace Processors for Physics-Based Application 391 -- 8.1 Subspace Identification of a Structural Device 391 -- 8.1.1 State-Space Vibrational Systems 392 -- 8.1.1.1 State-Space Realization 394 -- 8.1.2 Deterministic State-Space Realizations 396 -- 8.1.2.1 Subspace Approach 396 -- 8.1.3 Vibrational System Processing 398 -- 8.1.4 Application: Vibrating Structural Device 400 -- 8.1.5 Summary 404 -- 8.2 MBID for Scintillator System Characterization 405 -- 8.2.1 Scintillation Pulse Shape Model 407 -- 8.2.2 Scintillator State-Space Model 409 -- 8.2.3 Scintillator Sampled-Data State-Space Model 410 -- 8.2.4 Gauss-Markov State-Space Model 411 -- 8.2.5 Identification of the Scintillator Pulse Shape Model 412 -- 8.2.6 Kalman Filter Design: Scintillation/Photomultiplier System 414 -- 8.2.6.1 Kalman Filter Design: Scintillation/Photomultiplier Data 416 -- 8.2.7 Summary 417 -- 8.3 Parametrically Adaptive Detection of Fission Processes 418 -- 8.3.1 Fission-Based Processing Model 419 -- 8.3.2 Interarrival Distribution 420 -- 8.3.3 Sequential Detection 422 -- 8.3.4 Sequential Processor 422 -- 8.3.5 Sequential Detection for Fission Processes 424 -- 8.3.6 Bayesian Parameter Estimation 426 -- 8.3.7 Sequential Bayesian Processor 427 -- 8.3.8 Particle Filter for Fission Processes 429 -- 8.3.9 SNM Detection and Estimation: Synthesized Data 430 -- 8.3.10 Summary 433 -- 8.4 Parametrically Adaptive Processing for Shallow Ocean Application 435 -- 8.4.1 State-Space Propagator 436 -- 8.4.2 State-Space Model 436 -- 8.4.2.1 Augmented State-Space Models 438 -- 8.4.3 Processors 441 -- 8.4.4 Model-Based Ocean Acoustic Processing 444 -- 8.4.4.1 Adaptive PF Design: Modal Coefficients 445 -- 8.4.4.2 Adaptive PF Design: Wavenumbers 447.
8.4.5 Summary 450 -- 8.5 MBID for Chirp Signal Extraction 452 -- 8.5.1 Chirp-like Signals 453 -- 8.5.1.1 Linear Chirp 453 -- 8.5.1.2 Frequency-Shift Key (FSK) Signal 455 -- 8.5.2 Model-Based Identification: Linear Chirp Signals 457 -- 8.5.2.1 Gauss-Markov State-Space Model: Linear Chirp 457 -- 8.5.3 Model-Based Identification: FSK Signals 459 -- 8.5.3.1 Gauss-Markov State-Space Model: FSK Signals 460 -- 8.5.4 Summary 462 -- References 462 -- Appendix A Probability and Statistics Overview 467 -- A.1 Probability Theory 467 -- A.2 Gaussian Random Vectors 473 -- A.3 Uncorrelated Transformation: Gaussian Random Vectors 473 -- A.4 Toeplitz Correlation Matrices 474 -- A.5 Important Processes 474 -- References 476 -- Appendix B Projection Theory 477 -- B.1 Projections: Deterministic Spaces 477 -- B.2 Projections: Random Spaces 478 -- B.3 Projection: Operators 479 -- B.3.1 Orthogonal (Perpendicular) Projections 479 -- B.3.2 Oblique (Parallel) Projections 481 -- References 483 -- Appendix C Matrix Decompositions 485 -- C.1 Singular-Value Decomposition 485 -- C.2 QR-Decomposition 487 -- C.3 LQ-Decomposition 487 -- References 488 -- Appendix D Output-Only Subspace Identification 489 -- References 492 -- Index 495.
Summary: "Provides a model-based "bridge" for signal processors/control engineers enabling a coupling and motivation for model development and subsequent processor designs/applications - Incorporates an in-depth treatment of the subspace approach that applies a variety of the subspace algorithm to synthesized examples and actual applications - Introduces new, fast subspace identifiers, capable of developing the required model for processing/controls Market description: Primary audience: advanced seniors, 1st year graduate student (engineering, sciences) Secondary audience: engineering professionals"-- Provided by publisher.
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Includes bibliographical references and index.

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 (Blackman-Tukey) 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 Gauss-Markov State-Space Models 83 -- 3.5.1 Discrete-Time Gauss-Markov 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) Gauss-Markov State-Space Models 97 -- 3.9 Summary 101 -- MATLAB Notes 102 -- References 102 -- Problems 103 -- 4 Model-Based Processors 107.

4.1 Introduction 107 -- 4.2 Linear Model-Based Processor: Kalman Filter 108 -- 4.2.1 Innovations Approach 110 -- 4.2.2 Bayesian Approach 114 -- 4.2.3 Innovations Sequence 116 -- 4.2.4 Practical Linear Kalman Filter Design: Performance Analysis 117 -- 4.2.5 Steady-State Kalman Filter 125 -- 4.2.6 Kalman Filter/Wiener Filter Equivalence 128 -- 4.3 Nonlinear State-Space Model-Based Processors 129 -- 4.3.1 Nonlinear Model-Based Processor: Linearized Kalman Filter 130 -- 4.3.2 Nonlinear Model-Based Processor: Extended Kalman Filter 133 -- 4.3.3 Nonlinear Model-Based Processor: Iterated-Extended Kalman Filter 138 -- 4.3.4 Nonlinear Model-Based Processor: Unscented Kalman Filter 141 -- 4.3.5 Practical Nonlinear Model-Based Processor Design: Performance Analysis 148 -- 4.3.6 Nonlinear Model-Based Processor: Particle Filter 151 -- 4.3.7 Practical Bayesian Model-Based Design: Performance Analysis 160 -- 4.4 Case Study: 2D-Tracking Problem 166 -- 4.5 Summary 173 -- MATLAB Notes 173 -- References 174 -- Problems 177 -- 5 Parametrically Adaptive Processors 185 -- 5.1 Introduction 185 -- 5.2 Parametrically Adaptive Processors: Bayesian Approach 186 -- 5.3 Parametrically Adaptive Processors: Nonlinear Kalman Filters 187 -- 5.3.1 Parametric Models 188 -- 5.3.2 Classical Joint State/Parametric Processors: Augmented Extended Kalman Filter 190 -- 5.3.3 Modern Joint State/Parametric Processor: Augmented Unscented Kalman Filter 198 -- 5.4 Parametrically Adaptive Processors: Particle Filter 201 -- 5.4.1 Joint State/Parameter Estimation: Particle Filter 201 -- 5.5 Parametrically Adaptive Processors: Linear Kalman Filter 208 -- 5.6 Case Study: Random Target Tracking 214 -- 5.7 Summary 222 -- MATLAB Notes 223 -- References 223 -- Problems 226 -- 6 Deterministic Subspace Identification 231 -- 6.1 Introduction 231 -- 6.2 Deterministic Realization Problem 232 -- 6.2.1 Realization Theory 233 -- 6.2.2 Balanced Realizations 238 -- 6.2.3 Systems Theory Summary 239 -- 6.3 Classical Realization 241.

6.3.1 Ho-Kalman Realization Algorithm 241 -- 6.3.2 SVD Realization Algorithm 243 -- 6.3.2.1 Realization: Linear Time-Invariant Mechanical Systems 246 -- 6.3.3 Canonical Realization 251 -- 6.3.3.1 Invariant System Descriptions 251 -- 6.3.3.2 Canonical Realization Algorithm 257 -- 6.4 Deterministic Subspace Realization: Orthogonal Projections 264 -- 6.4.1 Subspace Realization: Orthogonal Projections 266 -- 6.4.2 Multivariable Output Error State-Space (MOESP) Algorithm 271 -- 6.5 Deterministic Subspace Realization: Oblique Projections 274 -- 6.5.1 Subspace Realization: Oblique Projections 278 -- 6.5.2 Numerical Algorithms for Subspace State-Space System Identification (N4SID) Algorithm 280 -- 6.6 Model Order Estimation and Validation 285 -- 6.6.1 Order Estimation: SVD Approach 286 -- 6.6.2 Model Validation 289 -- 6.7 Case Study: Structural Vibration Response 295 -- 6.8 Summary 299 -- MATLAB Notes 300 -- References 300 -- Problems 303 -- 7 Stochastic Subspace Identification 309 -- 7.1 Introduction 309 -- 7.2 Stochastic Realization Problem 312 -- 7.2.1 Correlated Gauss-Markov Model 312 -- 7.2.2 Gauss-Markov Power Spectrum 313 -- 7.2.3 Gauss-Markov Measurement Covariance 314 -- 7.2.4 Stochastic Realization Theory 315 -- 7.3 Classical Stochastic Realization via the Riccati Equation 317 -- 7.4 Classical Stochastic Realization via Kalman Filter 321 -- 7.4.1 Innovations Model 321 -- 7.4.2 Innovations Power Spectrum 322 -- 7.4.3 Innovations Measurement Covariance 323 -- 7.4.4 Stochastic Realization: Innovations Model 325 -- 7.5 Stochastic Subspace Realization: Orthogonal Projections 330 -- 7.5.1 Multivariable Output Error State-SPace (MOESP) Algorithm 334 -- 7.6 Stochastic Subspace Realization: Oblique Projections 342 -- 7.6.1 Numerical Algorithms for Subspace State-Space System Identification (N4SID) Algorithm 346 -- 7.6.2 Relationship: Oblique (N4SID) and Orthogonal (MOESP) Algorithms 351 -- 7.7 Model Order Estimation and Validation 353 -- 7.7.1 Order Estimation: Stochastic Realization Problem 354.

7.7.1.1 Order Estimation: Statistical Methods 356 -- 7.7.2 Model Validation 362 -- 7.7.2.1 Residual Testing 363 -- 7.8 Case Study: Vibration Response of a Cylinder: Identification and Tracking 369 -- 7.9 Summary 378 -- MATLAB NOTES 378 -- References 379 -- Problems 382 -- 8 Subspace Processors for Physics-Based Application 391 -- 8.1 Subspace Identification of a Structural Device 391 -- 8.1.1 State-Space Vibrational Systems 392 -- 8.1.1.1 State-Space Realization 394 -- 8.1.2 Deterministic State-Space Realizations 396 -- 8.1.2.1 Subspace Approach 396 -- 8.1.3 Vibrational System Processing 398 -- 8.1.4 Application: Vibrating Structural Device 400 -- 8.1.5 Summary 404 -- 8.2 MBID for Scintillator System Characterization 405 -- 8.2.1 Scintillation Pulse Shape Model 407 -- 8.2.2 Scintillator State-Space Model 409 -- 8.2.3 Scintillator Sampled-Data State-Space Model 410 -- 8.2.4 Gauss-Markov State-Space Model 411 -- 8.2.5 Identification of the Scintillator Pulse Shape Model 412 -- 8.2.6 Kalman Filter Design: Scintillation/Photomultiplier System 414 -- 8.2.6.1 Kalman Filter Design: Scintillation/Photomultiplier Data 416 -- 8.2.7 Summary 417 -- 8.3 Parametrically Adaptive Detection of Fission Processes 418 -- 8.3.1 Fission-Based Processing Model 419 -- 8.3.2 Interarrival Distribution 420 -- 8.3.3 Sequential Detection 422 -- 8.3.4 Sequential Processor 422 -- 8.3.5 Sequential Detection for Fission Processes 424 -- 8.3.6 Bayesian Parameter Estimation 426 -- 8.3.7 Sequential Bayesian Processor 427 -- 8.3.8 Particle Filter for Fission Processes 429 -- 8.3.9 SNM Detection and Estimation: Synthesized Data 430 -- 8.3.10 Summary 433 -- 8.4 Parametrically Adaptive Processing for Shallow Ocean Application 435 -- 8.4.1 State-Space Propagator 436 -- 8.4.2 State-Space Model 436 -- 8.4.2.1 Augmented State-Space Models 438 -- 8.4.3 Processors 441 -- 8.4.4 Model-Based Ocean Acoustic Processing 444 -- 8.4.4.1 Adaptive PF Design: Modal Coefficients 445 -- 8.4.4.2 Adaptive PF Design: Wavenumbers 447.

8.4.5 Summary 450 -- 8.5 MBID for Chirp Signal Extraction 452 -- 8.5.1 Chirp-like Signals 453 -- 8.5.1.1 Linear Chirp 453 -- 8.5.1.2 Frequency-Shift Key (FSK) Signal 455 -- 8.5.2 Model-Based Identification: Linear Chirp Signals 457 -- 8.5.2.1 Gauss-Markov State-Space Model: Linear Chirp 457 -- 8.5.3 Model-Based Identification: FSK Signals 459 -- 8.5.3.1 Gauss-Markov State-Space Model: FSK Signals 460 -- 8.5.4 Summary 462 -- References 462 -- Appendix A Probability and Statistics Overview 467 -- A.1 Probability Theory 467 -- A.2 Gaussian Random Vectors 473 -- A.3 Uncorrelated Transformation: Gaussian Random Vectors 473 -- A.4 Toeplitz Correlation Matrices 474 -- A.5 Important Processes 474 -- References 476 -- Appendix B Projection Theory 477 -- B.1 Projections: Deterministic Spaces 477 -- B.2 Projections: Random Spaces 478 -- B.3 Projection: Operators 479 -- B.3.1 Orthogonal (Perpendicular) Projections 479 -- B.3.2 Oblique (Parallel) Projections 481 -- References 483 -- Appendix C Matrix Decompositions 485 -- C.1 Singular-Value Decomposition 485 -- C.2 QR-Decomposition 487 -- C.3 LQ-Decomposition 487 -- References 488 -- Appendix D Output-Only Subspace Identification 489 -- References 492 -- Index 495.

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"Provides a model-based "bridge" for signal processors/control engineers enabling a coupling and motivation for model development and subsequent processor designs/applications - Incorporates an in-depth treatment of the subspace approach that applies a variety of the subspace algorithm to synthesized examples and actual applications - Introduces new, fast subspace identifiers, capable of developing the required model for processing/controls Market description: Primary audience: advanced seniors, 1st year graduate student (engineering, sciences) Secondary audience: engineering professionals"-- Provided by publisher.

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