Optimal Filtering

Name: Optimal Filtering
Brand: Dover Publications
Price: 51.39 EUR
Availability: OnlineOnly

Brian D. O. Anderson John B. Moore(Autor*in)

Dover Publications (Verlag)

1. Auflage

Erschienen am 23. Mai 2012

695 Seiten

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978-0-486-13689-9 (ISBN)

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Inhalt

Intro
Title Page
Copyright Page
Table of Contents
PREFACE
CHAPTER 1 - INTRODUCTION
1.1 FILTERING
1.2 HISTORY OF SIGNAL FILTERING
1.3 SUBJECT MATTER OF THIS BOOK
1.4 OUTLINE OF THE BOOK
REFERENCES
CHAPTER 2 - FILTERING, LINEAR SYSTEMS, AND ESTIMATION
2.1 SYSTEMS, NOISE, FILTERING, SMOOTHING, AND PREDICTION
2.2 THE GAUSS-MARKOV DISCRETE-TIME MODEL
2.3 ESTIMATION CRITERIA
REFERENCES
CHAPTER 3 - THE DISCRETE-TIME
3.1 THE KALMAN FILTER
3.2 BEST LINEAR ESTIMATOR PROPERTY OF THE KALMAN FILTER
3.3 IDENTIFICATION AS A KALMAN FILTERING PROBLEM
3.4 APPLICATION OF KALMAN FILTERS
REFERENCES
CHAPTER 4 - TIME-INVARIANT FILTERS
4.1 BACKGROUND TO TIME INVARIANCE OF THE FILTER
4.2 STABILITY PROPERTIES OF LINEAR, DISCRETE-TIME SYSTEMS
4.3 STATIONARY BEHAVIOUR OF LINEAR SYSTEMS
4.4 TIME INVARIANCE AND ASYMPTOTIC STABILITY OF THE FILTER
4.5 FREQUENCY DOMAIN FORMULAS
REFERENCES
CHAPTER 5 - KALMAN FILTER PROPERTIES
5.1 INTRODUCTION
5.2 MINIMUM VARIANCE AND LINEAR MINIMUM VARIANCE ESTIMATION
ORTHOGONALITY AND PROJECTION
5.3 THE INNOVATIONS SEQUENCE
5.4 THE KALMAN FILTER
5.5 TRUE FILTERED ESTIMATES AND THE SIGNAL-TO-NOISE RATIO IMPROVEMENT PROPERTY
5.6 INVERSE PROBLEMS: WHEN IS A FILTER OPTIMAL?
REFERENCES
CHAPTER 6 - COMPUTATIONAL ASPECTS
6.1 SIGNAL MODEL ERRORS, FILTER DIVERGENCE, AND DATA SATURATION
6.2 EXPONENTIAL DATA WEIGHTING-A FILTER WITH PRESCRIBED DEGREE OF STABILITY
6.3 THE MATRIX INVERSION LEMMA AND THE INFORMATION FILTER
6.4 SEQUENTIAL PROCESSING
6.5 SQUARE ROOT FILTERING
6.6 THE HIGH MEASUREMENT NOISE CASE
6.7 CHANDRASEKHAR-TYPE, DOUBLING, AND NONRECURSIVE ALGORITHMS
REFERENCES
CHAPTER 7 - SMOOTHING OF DISCRETE-TIME SIGNALS
7.1 INTRODUCTION TO SMOOTHING
7.2 FIXED-POINT SMOOTHING
7.3 FIXED-LAG SMOOTHING
7.4 FIXED-INTERVAL SMOOTHING
REFERENCES
CHAPTER 8 - APPLICATIONS IN NONLINEAR FILTERING
8.1 NONLINEAR FILTERING
8.2 THE EXTENDED KALMAN FILTER
8.3 A BOUND OPTIMAL FILTER
8.4 GAUSSIAN SUM ESTIMATORS
REFERENCES
CHAPTER 9 - INNOVATIONS REPRESENTATIONS, SPECTRAL FACTORIZATION, WIENER AND LEVINSON FILTERING
9.1 INTRODUCTION
9.2 KALMAN FILTER DESIGN FROM COVARIANCE DATA
9.3 INNOVATIONS REPRESENTATIONS WITH FINITE INITIAL TIME
9.4 STATIONARY INNOVATIONS REPRESENTATIONS AND SPECTRAL FACTORIZATION
9.5 WIENER FILTERING
9.6 LEVINSON FILTERS
REFERENCES
CHAPTER 10 - PARAMETER IDENTIFICATION AND ADAPTIVE ESTIMATION
10.1 ADAPTIVE ESTIMATION VIA PARALLEL PROCESSING
10.2 ADAPTIVE ESTIMATION VIA EXTENDED LEAST SQUARES
REFERENCES
CHAPTER 11 - COLORED NOISE AND SUBOPTIMAL REDUCED ORDER FILTERS
11.1 GENERAL APPROACHES TO DEALING WITH COLORED NOISE
11.2 FILTER DESIGN WITH MARKOV OUTPUT NOISE
11.3 FILTER DESIGN WITH SINGULAR OR NEAR-SINGULAR OUTPUT NOISE
11.4 SUBOPTIMAL DESIGN GIVEN COLORED INPUT OR MEASUREMENT NOISE
11.5 SUBOPTIMAL FILTER DESIGN BY MODEL ORDER REDUCTION
REFERENCES
APPENDIX A - BRIEF REVIEW OF RESULTS OF PROBABILITY THEORY
APPENDIX B - BRIEF REVIEW OF SOME RESULTS OF MATRIX THEORY
APPENDIX C - BRIEF REVIEW OF SEVERAL MAJOR RESULTS OF LINEAR SYSTEM THEORY
APPENDIX D - LYAPUNOV STABILITY
AUTHOR INDEX
SUBJECT INDEX

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