Muutke küpsiste eelistusi

E-raamat: Model-Based Processing: An Applied Subspace Identification Approach

(University of California, Lawrence Livermore National Laboratory)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 15-Mar-2019
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119457787
Teised raamatud teemal:
  • Formaat - EPUB+DRM
  • Hind: 149,37 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Raamatukogudele
Model-Based Processing: An Applied Subspace Identification ApproachSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 15-Mar-2019
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119457787
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

"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 yeargraduate student (engineering, sciences) Secondary audience: engineering professionals"--

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. 

Preface xiii
Acknowledgements xxi
Glossary xxiii
1 Introduction
1(28)
1.1 Background
1(1)
1.2 Signal Estimation
2(6)
1.3 Model-Based Processing
8(8)
1.4 Model-Based Identification
16(4)
1.5 Subspace Identification
20(2)
1.6 Notation and Terminology
22(2)
1.7 Summary
24(1)
Matlab Notes
25(1)
References
25(1)
Problems
26(3)
2 Random Signals and Systems
29(40)
2.1 Introduction
29(3)
2.2 Discrete Random Signals
32(4)
2.3 Spectral Representation of Random Signals
36(4)
2.4 Discrete Systems with Random Inputs
40(4)
2.4.1 Spectral Theorems
41(1)
2.4.2 ARMAX Modeling
42(2)
2.5 Spectral Estimation
44(15)
2.5.1 Classical (Nonparametric) Spectral Estimation
44(1)
2.5.1.1 Correlation Method (Blackman--Tukey)
45(1)
2.5.1.2 Average Periodogram Method (Welch)
46(1)
2.5.2 Modern (Parametric) Spectral Estimation
47(1)
2.5.2.1 Autoregressive (All-Pole) Spectral Estimation
48(3)
2.5.2.2 Autoregressive Moving Average Spectral Estimation
51(1)
2.5.2.3 Minimum Variance Distortionless Response (MVDR) Spectral Estimation
52(3)
2.5.2.4 Multiple Signal Classification (MUSIC) Spectral Estimation
55(4)
2.6 Case Study: Spectral Estimation of Bandpass Sinusoids
59(2)
2.7 Summary
61(1)
Matlab Notes
61(1)
References
62(2)
Problems
64(5)
3 State-Space Models for Identification
69(38)
3.1 Introduction
69(1)
3.2 Continuous-Time State-Space Models
69(4)
3.3 Sampled-Data State-Space Models
73(1)
3.4 Discrete-Time State-Space Models
74(9)
3.4.1 Linear Discrete Time-Invariant Systems
77(1)
3.4.2 Discrete Systems Theory
78(4)
3.4.3 Equivalent Linear Systems
82(1)
3.4.4 Stable Linear Systems
83(1)
3.5 Gauss--Markov State-Space Models
83(6)
3.5.1 Discrete-Time Gauss--Markov Models
83(6)
3.6 Innovations Model
89(1)
3.7 State-Space Model Structures
90(7)
3.7.1 Time-Series Models
91(1)
3.7.2 State-Space and Time-Series Equivalence Models
91(6)
3.8 Nonlinear (Approximate) Gauss--Markov State-Space Models
97(4)
3.9 Summary
101(1)
Matlab Notes
102(1)
References
102(1)
Problems
103(4)
4 Model-Based Processors
107(78)
4.1 Introduction
107(1)
4.2 Linear Model-Based Processor: Kalman Filter
108(21)
4.2.1 Innovations Approach
110(4)
4.2.2 Bayesian Approach
114(2)
4.2.3 Innovations Sequence
116(1)
4.2.4 Practical Linear Kalman Filter Design: Performance Analysis
117(8)
4.2.5 Steady-State Kalman Filter
125(3)
4.2.6 Kalman Filter/Wiener Filter Equivalence
128(1)
4.3 Nonlinear State-Space Model-Based Processors
129(37)
4.3.1 Nonlinear Model-Based Processor: Linearized Kalman Filter
130(3)
4.3.2 Nonlinear Model-Based Processor: Extended Kalman Filter
133(5)
4.3.3 Nonlinear Model-Based Processor: Iterated-Extended Kalman Filter
138(3)
4.3.4 Nonlinear Model-Based Processor: Unscented Kalman Filter
141(7)
4.3.5 Practical Nonlinear Model-Based Processor Design: Performance Analysis
148(3)
4.3.6 Nonlinear Model-Based Processor: Particle Filter
151(9)
4.3.7 Practical Bayesian Model-Based Design: Performance Analysis
160(6)
4.4 Case Study: 2D-Tracking Problem
166(7)
4.5 Summary
173(1)
Matlab Notes
173(1)
References
174(3)
Problems
177(8)
5 Parametrically Adaptive Processors
185(46)
5.1 Introduction
185(1)
5.2 Parametrically Adaptive Processors: Bayesian Approach
186(1)
5.3 Parametrically Adaptive Processors: Nonlinear Kalman Filters
187(14)
5.3.1 Parametric Models
188(2)
5.3.2 Classical Joint State/Parametric Processors: Augmented Extended Kalman Filter
190(8)
5.3.3 Modern Joint State/Parametric Processor: Augmented Unscented Kalman Filter
198(3)
5.4 Parametrically Adaptive Processors: Particle Filter
201(7)
5.4.1 Joint State/Parameter Estimation: Particle Filter
201(7)
5.5 Parametrically Adaptive Processors: Linear Kalman Filter
208(6)
5.6 Case Study: Random Target Tracking
214(8)
5.7 Summary
222(1)
Matlab Notes
223(1)
References
223(3)
Problems
226(5)
6 Deterministic Subspace Identification
231(78)
6.1 Introduction
231(1)
6.2 Deterministic Realization Problem
232(9)
6.2.1 Realization Theory
233(5)
6.2.2 Balanced Realizations
238(1)
6.2.3 Systems Theory Summary
239(2)
6.3 Classical Realization
241(23)
6.3.1 Ho--Kalman Realization Algorithm
241(2)
6.3.2 SVD Realization Algorithm
243(3)
6.3.2.1 Realization: Linear Time-Invariant Mechanical Systems
246(5)
6.3.3 Canonical Realization
251(1)
6.3.3.1 Invariant System Descriptions
251(6)
6.3.3.2 Canonical Realization Algorithm
257(7)
6.4 Deterministic Subspace Realization: Orthogonal Projections
264(10)
6.4.1 Subspace Realization: Orthogonal Projections
266(5)
6.4.2 Multivariable Output Error State-Space (MOESP) Algorithm
271(3)
6.5 Deterministic Subspace Realization: Oblique Projections
274(11)
6.5.1 Subspace Realization: Oblique Projections
278(2)
6.5.2 Numerical Algorithms for Subspace State-Space System Identification (N4SID) Algorithm
280(5)
6.6 Model Order Estimation and Validation
285(10)
6.6.1 Order Estimation: SVD Approach
286(3)
6.6.2 Model Validation
289(6)
6.7 Case Study: Structural Vibration Response
295(4)
6.8 Summary
299(1)
zMatlab Notes
300(1)
References
300(3)
Problems
303(6)
7 Stochastic Subspace Identification
309(82)
7.1 Introduction
309(3)
7.2 Stochastic Realization Problem
312(5)
7.2.1 Correlated Gauss--Markov Model
312(1)
7.2.2 Gauss--Markov Power Spectrum
313(1)
7.2.3 Gauss--Markov Measurement Covariance
314(1)
7.2.4 Stochastic Realization Theory
315(2)
7.3 Classical Stochastic Realization via the Riccati Equation
317(4)
7.4 Classical Stochastic Realization via Kalman Filter
321(9)
7.4.1 Innovations Model
321(1)
7.4.2 Innovations Power Spectrum
322(1)
7.4.3 Innovations Measurement Covariance
323(2)
7.4.4 Stochastic Realization: Innovations Model
325(5)
7.5 Stochastic Subspace Realization: Orthogonal Projections
330(12)
7.5.1 Multivariable Output Error State-Space (MOESP) Algorithm
334(8)
7.6 Stochastic Subspace Realization: Oblique Projections
342(11)
7.6.1 Numerical Algorithms for Subspace State-Space System Identification (N4SID) Algorithm
346(5)
7.6.2 Relationship: Oblique (N4SID) and Orthogonal (MOESP) Algorithms
351(2)
7.7 Model Order Estimation and Validation
353(16)
7.7.1 Order Estimation: Stochastic Realization Problem
354(2)
7.7.1.1 Order Estimation: Statistical Methods
356(6)
7.7.2 Model Validation
362(1)
7.7.2.1 Residual Testing
363(6)
7.8 Case Study: Vibration Response of a Cylinder: Identification and Tracking
369(9)
7.9 Summary
378(13)
Matlab Notes 378(1)
References 379(3)
Problems 382(113)
8 Subspace Processors for Physics-Based Application
391(76)
8.1 Subspace Identification of a Structural Device
391(14)
8.1.1 State-Space Vibrational Systems
392(2)
8.1.1.1 State-Space Realization
394(2)
8.1.2 Deterministic State-Space Realizations
396(1)
8.1.2.1 Subspace Approach
396(2)
8.1.3 Vibrational System Processing
398(2)
8.1.4 Application: Vibrating Structural Device
400(4)
8.1.5 Summary
404(1)
8.2 MBID for Scintillator System Characterization
405(13)
8.2.1 Scintillation Pulse Shape Model
407(2)
8.2.2 Scintillator State-Space Model
409(1)
8.2.3 Scintillator Sampled-Data State-Space Model
410(1)
8.2.4 Gauss--Markov State-Space Model
411(1)
8.2.5 Identification of the Scintillator Pulse Shape Model
412(2)
8.2.6 Kalman Filter Design: Scintillation/Photomultiplier System
414(2)
8.2.6.1 Kalman Filter Design: Scintillation/Photomultiplier Data
416(1)
8.2.7 Summary
417(1)
8.3 Parametrically Adaptive Detection of Fission Processes
418(17)
8.3.1 Fission-Based Processing Model
419(1)
8.3.2 Interarrival Distribution
420(2)
8.3.3 Sequential Detection
422(1)
8.3.4 Sequential Processor
422(2)
8.3.5 Sequential Detection for Fission Processes
424(2)
8.3.6 Bayesian Parameter Estimation
426(1)
8.3.7 Sequential Bayesian Processor
427(2)
8.3.8 Particle Filter for Fission Processes
429(1)
8.3.9 SNM Detection and Estimation: Synthesized Data
430(3)
8.3.10 Summary
433(2)
8.4 Parametrically Adaptive Processing for Shallow Ocean Application
435(17)
8.4.1 State-Space Propagator
436(1)
8.4.2 State-Space Model
436(2)
8.4.2.1 Augmented State-Space Models
438(3)
8.4.3 Processors
441(3)
8.4.4 Model-Based Ocean Acoustic Processing
444(1)
8.4.4.1 Adaptive PF Design: Modal Coefficients
445(2)
8.4.4.2 Adaptive PF Design: Wavenumbers
447(3)
8.4.5 Summary
450(2)
8.5 MBID for Chirp Signal Extraction
452(10)
8.5.1 Chirp-like Signals
453(1)
8.5.1.1 Linear Chirp
453(2)
8.5.1.2 Frequency-Shift Key (FSK) Signal
455(2)
8.5.2 Model-Based Identification: Linear Chirp Signals
457(1)
8.5.2.1 Gauss--Markov State-Space Model: Linear Chirp
457(2)
8.5.3 Model-Based Identification: FSK Signals
459(1)
8.5.3.1 Gauss--Markov State-Space Model: FSK Signals
460(2)
8.5.4 Summary
462(1)
References
462(5)
Appendix A Probability and Statistics Overview
467(10)
A.1 Probability Theory
467(6)
A.2 Gaussian Random Vectors
473(1)
A.3 Uncorrelated Transformation: Gaussian Random Vectors
473(1)
A.4 Toeplitz Correlation Matrices
474(1)
A.5 Important Processes
474(2)
References
476(1)
Appendix B Projection Theory
477(8)
B.1 Projections: Deterministic Spaces
477(1)
B.2 Projections: Random Spaces
478(1)
B.3 Projection: Operators
479(4)
B.3.1 Orthogonal (Perpendicular) Projections
479(2)
B.3.2 Oblique (Parallel) Projections
481(2)
References
483(2)
Appendix C Matrix Decompositions
485(4)
C.1 Singular-Value Decomposition
485(2)
C.2 QR-Decomposition
487(1)
C.3 LQ-Decomposition
487(1)
References
488(1)
Appendix D Output-Only Subspace Identification
489(6)
References
492(3)
Index 495
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.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97811194577876e.html
Märksõnad: