Stochastic models, estimation and control.: Volume 1 /
Stochastic Models: Estimation and Control: v. 1.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York :
Academic Press,
1979.
|
| Schriftenreihe: | Mathematics in science and engineering ;
v. 141a. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Stochastic Models: Estimation and Control: v. 1. |
| Beschreibung: | 1 online resource (xix, 423 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9780080956503 0080956505 |
Internformat
MARC
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| 490 | 1 | |a Mathematics in science and engineering ; |v v. 141a | |
| 504 | |a Includes bibliographical references and index. | ||
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| 505 | 0 | |a Front Cover; Stochastic models, estimation, and control; Copyright Page; Contents; Preface; Contents of Volume 2; Notation; Chapter 1. Introduction; 1.1 Why Stochastic Models, Estimation, and Control?; 1.2 Overview of the Text; 1.3 The Kalman Filter: An Introduction to Concepts; 1.4 Basic Assumptions; 1.5 A Simple Example; 1.6 A Preview; General References; Appendix and Problems; References; Chapter 2. Deterministic system models; 2.1 Introduction; 2.2 Continuous-Time Dynamic Models; 2.3 Solutions to State Differential Equations; 2.4 Discrete-Time Measurements | |
| 505 | 8 | |a 2.5 Controllability and Observability2.6 Summary; References; Problems; Chapter 3. Probability theory and static models; 3.1 Introduction; 3.2 Probability and Random Variables; 3.3 Probability Distributions and Densities; 3.4 Conditional Probability and Densities; 3.5 Functions of Random Variables; 3.6 Expectation and Moments of Random Variables; 3.7 Conditional Expectations; 3.8 Characteristic Functions; 3.9 Gaussian Random Vectors; 3.10 Linear Operations on Gaussian Random Variables; 3.11 Estimation with Static Linear Gaussian System Models; 3.12 Summary; References; Problems | |
| 505 | 8 | |a Chapter 4. Stochastic processes and linear dynamic system models4.1 Introduction; 4.2 Stochastic Processes; 4.3 Stationary Stochastic Processes and Power Spectral Density; 4.4 System Modeling: Objectives and Directions; 4.5 Foundations: White Gaussian Noise and Brownian Motion; 4.6 Stochastic Integrals; 4.7 Stochastic Differentials; 4.8 Linear Stochastic Differential Equations; 4.9 Linear Stochastic Difference Equations; 4.10 The Overall System Model; 4.11 Shaping Filters and State Augmentation; 4.12 Power Spectrum Concepts and Shaping Filters; 4.13 Generating Practical System Models | |
| 505 | 8 | |a 4.14 SummaryReferences; Problems; Chapter 5. Optimal filtering with linear system models; 5.1 Introduction; 5.2 Problem Formulation; 5.3 The Discrete-Time (Sampled Data) Optimal Estimator: The Kalman Filter; 5.4 Statistics of Processes within the Filter Structure; 5.5 Other Criteria of Optimality; 5.6 Covariance Measurement Update Computations; 5.7 Inverse Covariance Form; 5.8 Stability; 5.9 Correlation of Dynamic Driving Noise and Measurement Noise; 5.10 Time-Correlated Measurement Noise: Perfect Measurements; 5.11 Continuous-Time Filter; 5.12 Wiener Filtering and Frequency Domain Techniques | |
| 505 | 8 | |a 5.13 SummaryReferences; Problems; Chapter 6. Design and performance analysis of Kalman filters; 6.1 Introduction; 6.2 The Requisite of Engineering Judgment; 6.3 Application of Kalman Filtering to Inertial Navigation Systems; 6.4 INS Aided by Position Data: A Simple Example; 6.5 Doppler-Aided INS; 6.6 INS Calibration and Alignment Using Direct Kalman Filter; 6.7 Generating Alternative Designs; 6.8 Performance (Sensitivity) Analysis; 6.9 Systematic Design Procedure; 6.10 INS Aided by Navigation Satellites; 6.11 Practical Aspects of Implementation; 6.12 Summary; References; Problems | |
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| 830 | 0 | |a Mathematics in science and engineering ; |v v. 141a. |0 http://id.loc.gov/authorities/names/n42015986 | |
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| author | Maybeck, Peter S. |
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| contents | Front Cover; Stochastic models, estimation, and control; Copyright Page; Contents; Preface; Contents of Volume 2; Notation; Chapter 1. Introduction; 1.1 Why Stochastic Models, Estimation, and Control?; 1.2 Overview of the Text; 1.3 The Kalman Filter: An Introduction to Concepts; 1.4 Basic Assumptions; 1.5 A Simple Example; 1.6 A Preview; General References; Appendix and Problems; References; Chapter 2. Deterministic system models; 2.1 Introduction; 2.2 Continuous-Time Dynamic Models; 2.3 Solutions to State Differential Equations; 2.4 Discrete-Time Measurements 2.5 Controllability and Observability2.6 Summary; References; Problems; Chapter 3. Probability theory and static models; 3.1 Introduction; 3.2 Probability and Random Variables; 3.3 Probability Distributions and Densities; 3.4 Conditional Probability and Densities; 3.5 Functions of Random Variables; 3.6 Expectation and Moments of Random Variables; 3.7 Conditional Expectations; 3.8 Characteristic Functions; 3.9 Gaussian Random Vectors; 3.10 Linear Operations on Gaussian Random Variables; 3.11 Estimation with Static Linear Gaussian System Models; 3.12 Summary; References; Problems Chapter 4. Stochastic processes and linear dynamic system models4.1 Introduction; 4.2 Stochastic Processes; 4.3 Stationary Stochastic Processes and Power Spectral Density; 4.4 System Modeling: Objectives and Directions; 4.5 Foundations: White Gaussian Noise and Brownian Motion; 4.6 Stochastic Integrals; 4.7 Stochastic Differentials; 4.8 Linear Stochastic Differential Equations; 4.9 Linear Stochastic Difference Equations; 4.10 The Overall System Model; 4.11 Shaping Filters and State Augmentation; 4.12 Power Spectrum Concepts and Shaping Filters; 4.13 Generating Practical System Models 4.14 SummaryReferences; Problems; Chapter 5. Optimal filtering with linear system models; 5.1 Introduction; 5.2 Problem Formulation; 5.3 The Discrete-Time (Sampled Data) Optimal Estimator: The Kalman Filter; 5.4 Statistics of Processes within the Filter Structure; 5.5 Other Criteria of Optimality; 5.6 Covariance Measurement Update Computations; 5.7 Inverse Covariance Form; 5.8 Stability; 5.9 Correlation of Dynamic Driving Noise and Measurement Noise; 5.10 Time-Correlated Measurement Noise: Perfect Measurements; 5.11 Continuous-Time Filter; 5.12 Wiener Filtering and Frequency Domain Techniques 5.13 SummaryReferences; Problems; Chapter 6. Design and performance analysis of Kalman filters; 6.1 Introduction; 6.2 The Requisite of Engineering Judgment; 6.3 Application of Kalman Filtering to Inertial Navigation Systems; 6.4 INS Aided by Position Data: A Simple Example; 6.5 Doppler-Aided INS; 6.6 INS Calibration and Alignment Using Direct Kalman Filter; 6.7 Generating Alternative Designs; 6.8 Performance (Sensitivity) Analysis; 6.9 Systematic Design Procedure; 6.10 INS Aided by Navigation Satellites; 6.11 Practical Aspects of Implementation; 6.12 Summary; References; Problems |
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| discipline | Informatik |
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| id | ZDB-4-EBA-ocn466443953 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:16:54Z |
| institution | BVB |
| isbn | 9780080956503 0080956505 |
| language | English |
| oclc_num | 466443953 |
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| physical | 1 online resource (xix, 423 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 1979 |
| publishDateSearch | 1979 |
| publishDateSort | 1979 |
| publisher | Academic Press, |
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| series | Mathematics in science and engineering ; |
| series2 | Mathematics in science and engineering ; |
| spelling | Maybeck, Peter S. http://id.loc.gov/authorities/names/n78061629 Stochastic models, estimation and control. Volume 1 / Peter S. Maybeck. New York : Academic Press, 1979. 1 online resource (xix, 423 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Mathematics in science and engineering ; v. 141a Includes bibliographical references and index. Print version record. Front Cover; Stochastic models, estimation, and control; Copyright Page; Contents; Preface; Contents of Volume 2; Notation; Chapter 1. Introduction; 1.1 Why Stochastic Models, Estimation, and Control?; 1.2 Overview of the Text; 1.3 The Kalman Filter: An Introduction to Concepts; 1.4 Basic Assumptions; 1.5 A Simple Example; 1.6 A Preview; General References; Appendix and Problems; References; Chapter 2. Deterministic system models; 2.1 Introduction; 2.2 Continuous-Time Dynamic Models; 2.3 Solutions to State Differential Equations; 2.4 Discrete-Time Measurements 2.5 Controllability and Observability2.6 Summary; References; Problems; Chapter 3. Probability theory and static models; 3.1 Introduction; 3.2 Probability and Random Variables; 3.3 Probability Distributions and Densities; 3.4 Conditional Probability and Densities; 3.5 Functions of Random Variables; 3.6 Expectation and Moments of Random Variables; 3.7 Conditional Expectations; 3.8 Characteristic Functions; 3.9 Gaussian Random Vectors; 3.10 Linear Operations on Gaussian Random Variables; 3.11 Estimation with Static Linear Gaussian System Models; 3.12 Summary; References; Problems Chapter 4. Stochastic processes and linear dynamic system models4.1 Introduction; 4.2 Stochastic Processes; 4.3 Stationary Stochastic Processes and Power Spectral Density; 4.4 System Modeling: Objectives and Directions; 4.5 Foundations: White Gaussian Noise and Brownian Motion; 4.6 Stochastic Integrals; 4.7 Stochastic Differentials; 4.8 Linear Stochastic Differential Equations; 4.9 Linear Stochastic Difference Equations; 4.10 The Overall System Model; 4.11 Shaping Filters and State Augmentation; 4.12 Power Spectrum Concepts and Shaping Filters; 4.13 Generating Practical System Models 4.14 SummaryReferences; Problems; Chapter 5. Optimal filtering with linear system models; 5.1 Introduction; 5.2 Problem Formulation; 5.3 The Discrete-Time (Sampled Data) Optimal Estimator: The Kalman Filter; 5.4 Statistics of Processes within the Filter Structure; 5.5 Other Criteria of Optimality; 5.6 Covariance Measurement Update Computations; 5.7 Inverse Covariance Form; 5.8 Stability; 5.9 Correlation of Dynamic Driving Noise and Measurement Noise; 5.10 Time-Correlated Measurement Noise: Perfect Measurements; 5.11 Continuous-Time Filter; 5.12 Wiener Filtering and Frequency Domain Techniques 5.13 SummaryReferences; Problems; Chapter 6. Design and performance analysis of Kalman filters; 6.1 Introduction; 6.2 The Requisite of Engineering Judgment; 6.3 Application of Kalman Filtering to Inertial Navigation Systems; 6.4 INS Aided by Position Data: A Simple Example; 6.5 Doppler-Aided INS; 6.6 INS Calibration and Alignment Using Direct Kalman Filter; 6.7 Generating Alternative Designs; 6.8 Performance (Sensitivity) Analysis; 6.9 Systematic Design Procedure; 6.10 INS Aided by Navigation Satellites; 6.11 Practical Aspects of Implementation; 6.12 Summary; References; Problems Stochastic Models: Estimation and Control: v. 1. System analysis. Control theory. http://id.loc.gov/authorities/subjects/sh85031658 Estimation theory. http://id.loc.gov/authorities/subjects/sh85044957 Systems Analysis https://id.nlm.nih.gov/mesh/D013597 Analyse de systèmes. Théorie de la commande. Théorie de l'estimation. systems analysis. aat SCIENCE System Theory. bisacsh TECHNOLOGY & ENGINEERING Operations Research. bisacsh Control theory fast Estimation theory fast System analysis fast has work: Volume 1 Stochastic models, estimation and control (Text) https://id.oclc.org/worldcat/entity/E39PCFHPfgH6pk4pCKXBPMkGXm https://id.oclc.org/worldcat/ontology/hasWork Print version: Maybeck, Peter S. Stochastic Models, Part A. New York : Academic Press, 1979 9780124807013 Mathematics in science and engineering ; v. 141a. http://id.loc.gov/authorities/names/n42015986 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=297125 Volltext |
| spellingShingle | Maybeck, Peter S. Stochastic models, estimation and control. Mathematics in science and engineering ; Front Cover; Stochastic models, estimation, and control; Copyright Page; Contents; Preface; Contents of Volume 2; Notation; Chapter 1. Introduction; 1.1 Why Stochastic Models, Estimation, and Control?; 1.2 Overview of the Text; 1.3 The Kalman Filter: An Introduction to Concepts; 1.4 Basic Assumptions; 1.5 A Simple Example; 1.6 A Preview; General References; Appendix and Problems; References; Chapter 2. Deterministic system models; 2.1 Introduction; 2.2 Continuous-Time Dynamic Models; 2.3 Solutions to State Differential Equations; 2.4 Discrete-Time Measurements 2.5 Controllability and Observability2.6 Summary; References; Problems; Chapter 3. Probability theory and static models; 3.1 Introduction; 3.2 Probability and Random Variables; 3.3 Probability Distributions and Densities; 3.4 Conditional Probability and Densities; 3.5 Functions of Random Variables; 3.6 Expectation and Moments of Random Variables; 3.7 Conditional Expectations; 3.8 Characteristic Functions; 3.9 Gaussian Random Vectors; 3.10 Linear Operations on Gaussian Random Variables; 3.11 Estimation with Static Linear Gaussian System Models; 3.12 Summary; References; Problems Chapter 4. Stochastic processes and linear dynamic system models4.1 Introduction; 4.2 Stochastic Processes; 4.3 Stationary Stochastic Processes and Power Spectral Density; 4.4 System Modeling: Objectives and Directions; 4.5 Foundations: White Gaussian Noise and Brownian Motion; 4.6 Stochastic Integrals; 4.7 Stochastic Differentials; 4.8 Linear Stochastic Differential Equations; 4.9 Linear Stochastic Difference Equations; 4.10 The Overall System Model; 4.11 Shaping Filters and State Augmentation; 4.12 Power Spectrum Concepts and Shaping Filters; 4.13 Generating Practical System Models 4.14 SummaryReferences; Problems; Chapter 5. Optimal filtering with linear system models; 5.1 Introduction; 5.2 Problem Formulation; 5.3 The Discrete-Time (Sampled Data) Optimal Estimator: The Kalman Filter; 5.4 Statistics of Processes within the Filter Structure; 5.5 Other Criteria of Optimality; 5.6 Covariance Measurement Update Computations; 5.7 Inverse Covariance Form; 5.8 Stability; 5.9 Correlation of Dynamic Driving Noise and Measurement Noise; 5.10 Time-Correlated Measurement Noise: Perfect Measurements; 5.11 Continuous-Time Filter; 5.12 Wiener Filtering and Frequency Domain Techniques 5.13 SummaryReferences; Problems; Chapter 6. Design and performance analysis of Kalman filters; 6.1 Introduction; 6.2 The Requisite of Engineering Judgment; 6.3 Application of Kalman Filtering to Inertial Navigation Systems; 6.4 INS Aided by Position Data: A Simple Example; 6.5 Doppler-Aided INS; 6.6 INS Calibration and Alignment Using Direct Kalman Filter; 6.7 Generating Alternative Designs; 6.8 Performance (Sensitivity) Analysis; 6.9 Systematic Design Procedure; 6.10 INS Aided by Navigation Satellites; 6.11 Practical Aspects of Implementation; 6.12 Summary; References; Problems System analysis. Control theory. http://id.loc.gov/authorities/subjects/sh85031658 Estimation theory. http://id.loc.gov/authorities/subjects/sh85044957 Systems Analysis https://id.nlm.nih.gov/mesh/D013597 Analyse de systèmes. Théorie de la commande. Théorie de l'estimation. systems analysis. aat SCIENCE System Theory. bisacsh TECHNOLOGY & ENGINEERING Operations Research. bisacsh Control theory fast Estimation theory fast System analysis fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85031658 http://id.loc.gov/authorities/subjects/sh85044957 https://id.nlm.nih.gov/mesh/D013597 |
| title | Stochastic models, estimation and control. |
| title_auth | Stochastic models, estimation and control. |
| title_exact_search | Stochastic models, estimation and control. |
| title_full | Stochastic models, estimation and control. Volume 1 / Peter S. Maybeck. |
| title_fullStr | Stochastic models, estimation and control. Volume 1 / Peter S. Maybeck. |
| title_full_unstemmed | Stochastic models, estimation and control. Volume 1 / Peter S. Maybeck. |
| title_short | Stochastic models, estimation and control. |
| title_sort | stochastic models estimation and control |
| topic | System analysis. Control theory. http://id.loc.gov/authorities/subjects/sh85031658 Estimation theory. http://id.loc.gov/authorities/subjects/sh85044957 Systems Analysis https://id.nlm.nih.gov/mesh/D013597 Analyse de systèmes. Théorie de la commande. Théorie de l'estimation. systems analysis. aat SCIENCE System Theory. bisacsh TECHNOLOGY & ENGINEERING Operations Research. bisacsh Control theory fast Estimation theory fast System analysis fast |
| topic_facet | System analysis. Control theory. Estimation theory. Systems Analysis Analyse de systèmes. Théorie de la commande. Théorie de l'estimation. systems analysis. SCIENCE System Theory. TECHNOLOGY & ENGINEERING Operations Research. Control theory Estimation theory System analysis |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=297125 |
| work_keys_str_mv | AT maybeckpeters stochasticmodelsestimationandcontrolvolume1 |