Estimation Problems in Hybrid Systems.

By: Sworder, David DContributor(s): Boyd, John EMaterial type: TextTextPublisher: Cambridge : Cambridge University Press, 1999Copyright date: ©1999Description: 1 online resource (294 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9780511149061Subject(s): Feedback control systemsGenre/Form: Electronic books.Additional physical formats: Print version:: Estimation Problems in Hybrid SystemsDDC classification: 629.83 LOC classification: TJ216 .S96 1999Online resources: Click to View
Contents:
Cover -- Half-title -- Title -- Copyright -- CONTENTS -- List of Illustrations -- Preface -- Who Should Read This Book -- Preliminaries -- Random Processes -- Stochastic Differential Equations -- Some Useful Results from Martingale Theory -- 1 Hybrid Estimation -- 1.1 Introduction -- 1.2 A Tracking Example -- 1.3 Infinite-Dimensional Algorithms -- 1.4 Multiple Model Algorithms -- 1.5 Modal Observations -- 2 The Polymorphic Estimator -- 2.1 Introduction -- 2.2 Modal Estimation -- 2.3 The Polymorphic Estimator -- 2.4 The Abridged PME: Time-Continuous Plant and Observation -- 3 Situation Assessment -- 3.1 Introduction to Situation Assessment -- 3.2 Decision Maker Dynamics -- 3.3 Order Bias in Human Decision Makers -- 3.4 Multilevel Situation Assessment -- 3.5 Decision-Making Phenotypes: An Example -- 3.6 Conclusions -- 4 Image-Enhanced Target Tracking -- 4.1 Tracking an Agile Target -- 4.2 Image Modeling and Interpretation -- 4.3 Tracking Maneuvering Targets -- 4.4 An Example: An Antiship Missile -- 4.5 Renewal Models for Maneuvering Targets -- 4.6 Performance Contrasts with Different Lifetime Modeling -- 5 Hybrid Plants with Base-State Discontinuities -- 5.1 Plant State Discontinuities -- 5.2 Plant State Rotation -- 5.3 A Maneuvering Aircraft with Variable Drag -- 5.4 Plant State Translation -- 5.5 A Maneuvering Aircraft with Sudden Translations -- 5.6 Variable Set Points -- 5.7 Estimating the Temperature of a Solar Panel -- 6 Mode-Dependent Observations -- 6.1 Problem Definition -- 6.2 The PME -- 6.3 Modal Estimation Using the PME -- 6.4 A Maneuvering Target Employing Countermeasures -- 7 Control of Hybrid Systems -- 7.1 Feedback Regulation of Hybrid Systems -- 7.2 The PME -- 7.3 An RPV Subject to Subsystem Failure -- 8 Target Recognition and Prediction -- 8.1 Problem Statement -- 8.2 Recognition and Tracking a Maneuvering Target.
8.3 Automatic Target Recognition -- 8.3.1 Engagement Nom-Agl -- 8.3.2 Engagement Nom-Lan -- 8.4 Path Prediction -- 8.5 A Missile Test in Australia -- 9 Hybrid Estimation Using Measure Changes -- 9.1 Change of Measure -- 9.2 Gaussian Minimum Shift Keying -- 9.3 An Example -- Appendix 1 PME Derivation Details -- A1.1 Introduction -- A1.2 Modal Estimation -- A1.2.1 Discussion -- A1.3 Base-State Estimation -- A1.3.1 Discussion -- A1.4 Some Mixed Moments -- A1.4.1 The Mixed Third Moment P -- A1.4.2 The Mixed Fourth Moment P -- A1.5 Error Dynamics -- A1.5.1 Discussion -- A1.5.2 The Predictable Quadratic Variation of {m} -- A1.5.3 The Predictable Cubic Variation of {m} -- A1.5.4 The Predictable Cubic Variation of {n} -- A1.5.5 Moment Identities -- A1.6 Base-State Covariance -- Discussion -- A1.7 Base-State Modal-State Cross Covariance -- A1.7.1 Discussion -- A1.8 A Mixed Third Central Moment -- A1.8.1 Discussion -- Appendix 2 COM Derivation Details -- BIBLIOGRAPHY -- INDEX -- GLOSSARY.
Summary: A guide to how diverse sensors can be integrated to create enhanced controllers and estimators in a variety of situations.
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Cover -- Half-title -- Title -- Copyright -- CONTENTS -- List of Illustrations -- Preface -- Who Should Read This Book -- Preliminaries -- Random Processes -- Stochastic Differential Equations -- Some Useful Results from Martingale Theory -- 1 Hybrid Estimation -- 1.1 Introduction -- 1.2 A Tracking Example -- 1.3 Infinite-Dimensional Algorithms -- 1.4 Multiple Model Algorithms -- 1.5 Modal Observations -- 2 The Polymorphic Estimator -- 2.1 Introduction -- 2.2 Modal Estimation -- 2.3 The Polymorphic Estimator -- 2.4 The Abridged PME: Time-Continuous Plant and Observation -- 3 Situation Assessment -- 3.1 Introduction to Situation Assessment -- 3.2 Decision Maker Dynamics -- 3.3 Order Bias in Human Decision Makers -- 3.4 Multilevel Situation Assessment -- 3.5 Decision-Making Phenotypes: An Example -- 3.6 Conclusions -- 4 Image-Enhanced Target Tracking -- 4.1 Tracking an Agile Target -- 4.2 Image Modeling and Interpretation -- 4.3 Tracking Maneuvering Targets -- 4.4 An Example: An Antiship Missile -- 4.5 Renewal Models for Maneuvering Targets -- 4.6 Performance Contrasts with Different Lifetime Modeling -- 5 Hybrid Plants with Base-State Discontinuities -- 5.1 Plant State Discontinuities -- 5.2 Plant State Rotation -- 5.3 A Maneuvering Aircraft with Variable Drag -- 5.4 Plant State Translation -- 5.5 A Maneuvering Aircraft with Sudden Translations -- 5.6 Variable Set Points -- 5.7 Estimating the Temperature of a Solar Panel -- 6 Mode-Dependent Observations -- 6.1 Problem Definition -- 6.2 The PME -- 6.3 Modal Estimation Using the PME -- 6.4 A Maneuvering Target Employing Countermeasures -- 7 Control of Hybrid Systems -- 7.1 Feedback Regulation of Hybrid Systems -- 7.2 The PME -- 7.3 An RPV Subject to Subsystem Failure -- 8 Target Recognition and Prediction -- 8.1 Problem Statement -- 8.2 Recognition and Tracking a Maneuvering Target.

8.3 Automatic Target Recognition -- 8.3.1 Engagement Nom-Agl -- 8.3.2 Engagement Nom-Lan -- 8.4 Path Prediction -- 8.5 A Missile Test in Australia -- 9 Hybrid Estimation Using Measure Changes -- 9.1 Change of Measure -- 9.2 Gaussian Minimum Shift Keying -- 9.3 An Example -- Appendix 1 PME Derivation Details -- A1.1 Introduction -- A1.2 Modal Estimation -- A1.2.1 Discussion -- A1.3 Base-State Estimation -- A1.3.1 Discussion -- A1.4 Some Mixed Moments -- A1.4.1 The Mixed Third Moment P -- A1.4.2 The Mixed Fourth Moment P -- A1.5 Error Dynamics -- A1.5.1 Discussion -- A1.5.2 The Predictable Quadratic Variation of {m} -- A1.5.3 The Predictable Cubic Variation of {m} -- A1.5.4 The Predictable Cubic Variation of {n} -- A1.5.5 Moment Identities -- A1.6 Base-State Covariance -- Discussion -- A1.7 Base-State Modal-State Cross Covariance -- A1.7.1 Discussion -- A1.8 A Mixed Third Central Moment -- A1.8.1 Discussion -- Appendix 2 COM Derivation Details -- BIBLIOGRAPHY -- INDEX -- GLOSSARY.

A guide to how diverse sensors can be integrated to create enhanced controllers and estimators in a variety of situations.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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