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Kalman filtering, theory and practice using MATLAB / Mohinder S. Grewal (2008)

Public Titre : Kalman filtering, theory and practice using MATLAB Type de document : Guide/Manuel Auteurs : Mohinder S. Grewal, Auteur ; Angus P. Andrews, Auteur Mention d'édition : third edition Editeur : New York, Londres, Hoboken (New Jersey), ... : John Wiley & Sons Année de publication : 2008 Importance : 575 p. Format : 16 x 24 cm + cédérom ISBN/ISSN/EAN : 978-0-470-17366-4 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] filtrage linéaire [Termes IGN] filtrage non linéaire [Termes IGN] filtre de Kalman [Termes IGN] GPS-INS [Termes IGN] Matlab [Termes IGN] positionnement par GNSS [Termes IGN] programmation stochastique [Termes IGN] système linéaire Index. décimale : 24.20 Traitement du signal Résumé : (Editeur) This book provides readers with a solid introduction to the theoretical and practical aspects of Kalman filtering. It has been updated with the latest developments in the implementation and application of Kalman filtering, including adaptations for nonlinear filtering, more robust smoothing methods, and developing applications in navigation. All software is provided in MATLAB, giving readers the opportunity to discover how the Kalman filter works in action and to consider the practical arithmetic needed to preserve the accuracy of results. Note de contenu : 1. General Information 1.1 On Kalman Filtering 1.2 On Optimal Estimation Methods 1.3 On the Notation Used In This Book 1.4 Summary Problems 2. Linear Dynamic Systems 2.1 Chapter Focus 2.2 Dynamic System Models 2.3 Continuous Linear Systems and Their Solutions 2.4 Discrete Linear Systems and Their Solutions 2.5 Observability of Linear Dynamic System Models 2.6 Summary Problems 3. Random Processes and Stochastic Systems 3.1 Chapter Focus 3.2 Probability and Random Variables (RVs) 3.3 Statistical Properties of RVs 3.4 Statistical Properties of Random Processes (RPs) 3.5 Linear RP Models 3.6 Shaping Filters and State Augmentation 3.7 Mean and Covariance Propagation 3.8 Relationships Between Model Parameters 3.9 Orthogonality Principle 3.10 Summary Problems 4. Linear Optimal Filters and Predictors 4.1 Chapter Focus 4.2 Kalman Filter 4.3 Kalman–Bucy Filter 4.4 Optimal Linear Predictors 4.5 Correlated Noise Sources 4.6 Relationships Between Kalman–Bucy and Wiener Filters 4.7 Quadratic Loss Functions 4.8 Matrix Riccati Differential Equation 4.9 Matrix Riccati Equation In Discrete Time 4.10 Model Equations for Transformed State Variables 4.11 Application of Kalman Filters 4.12 Summary Problems 5. Optimal Smoothers 5.1 Chapter Focus 5.2 Fixed-Interval Smoothing 5.3 Fixed-Lag Smoothing 5.4 Fixed-Point Smoothing 5.5 Summary Problems 6. Implementation Methods 6.1 Chapter Focus 6.2 Computer Roundoff 6.3 Effects of Roundoff Errors on Kalman Filters 6.4 Factorization Methods for Square-Root Filtering 6.5 Square-Root and UD Filters 6.6 Other Implementation Methods 6.7 Summary Problems 7. Nonlinear Filtering 7.1 Chapter Focus 7.2 Quasilinear Filtering 7.3 Sampling Methods for Nonlinear Filtering 7.4 Summary Problems 8. Practical Considerations 8.1 Chapter Focus 8.2 Detecting and Correcting Anomalous Behavior 8.3 Prefiltering and Data Rejection Methods 8.4 Stability of Kalman Filters 8.5 Suboptimal and Reduced-Order Filters 8.6 Schmidt–Kalman Filtering 8.7 Memory, Throughput, and Wordlength Requirements 8.8 Ways to Reduce Computational Requirements 8.9 Error Budgets and Sensitivity Analysis 8.10 Optimizing Measurement Selection Policies 8.11 Innovations Analysis 8.12 Summary Problems 9. Applications to Navigation 9.1 Chapter Focus 9.2 Host Vehicle Dynamics 9.3 Inertial Navigation Systems (INS) 9.4 Global Navigation Satellite Systems (GNSS) 9.5 Kalman Filters for GNSS 9.6 Loosely Coupled GNSS/INS Integration 9.7 Tightly Coupled GNSS/INS Integration 9.8 Summary Problems Appendix A - MATLAB Software A.1 Notice A.2 General System Requirements A.3 CD Directory Structure A.4 MATLAB Software for Chapter 2 A.5 MATLAB Software for Chapter 3 A.6 MATLAB Software for Chapter 4 A.7 MATLAB Software for Chapter 5 A.8 MATLAB Software for Chapter 6 A.9 MATLAB Software for Chapter 7 A.10 MATLAB Software for Chapter 8 A.11 MATLAB Software for Chapter 9 A.12 Other Sources of Software Appendix B - A Matrix Refresher B.1 Matrix Forms B.2 Matrix Operations B.3 Block Matrix Formulas B.4 Functions of Square Matrices B.5 Norms B.6 Cholesky Decomposition B.7 Orthogonal Decompositions of Matrices B.8 Quadratic Forms B.9 Derivatives of Matrices Numéro de notice : 22103 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Manuel Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=63231

Global Positioning Systems, inertial navigation, and integration / Mohinder S. Grewal (2001)

Public Titre : Global Positioning Systems, inertial navigation, and integration Type de document : Guide/Manuel Auteurs : Mohinder S. Grewal, Auteur ; Lawrence R. Weill, Auteur ; Angus P. Andrews, Auteur Editeur : New York, Londres, Hoboken (New Jersey), ... : John Wiley & Sons Année de publication : 2001 Importance : 392 p. Format : 16 x 24 cm + disquette ISBN/ISSN/EAN : 978-0-471-35032-3 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] centrale inertielle [Termes IGN] filtre de Kalman [Termes IGN] géodésie spatiale [Termes IGN] Global Orbitography Navigation Satellite System [Termes IGN] Global Positioning System [Termes IGN] GPS en mode différentiel [Termes IGN] GPS-INS [Termes IGN] modèle d'erreur [Termes IGN] navigation inertielle [Termes IGN] poursuite de satellite [Termes IGN] traitement du signal [Termes IGN] transformation de coordonnées Index. décimale : 30.64 GPS et centrales inertielles Résumé : (Editeur) The only comprehensive guide to Kalman filtering and its applications to real-world GPS/INS problems. Written by recognized authorities in the field, this book provides engineers, computer scientists, and others with a working familiarity with the theory and contemporary applications of Global Positioning Systems (GPS). Inertial Navigational Systems, and Kalman filters. Throughout, the focus is on solving real-work problems, with an emphasis on the effective use of state-of-the-art integration techniques for those systems, especially the application of Kalman filtering, To that end, the authors explore the various subtleties, common failures, and inherent limitations of the theory as it applies to real-world situations, and provide numerous detailed application examples and practice problems, including GPS-aided INS, modeling of gyros and accelerometers, and WAAS and LAAS. Drawing upon their many years of experience with GPS, INS and the Kalman filter, the authors present numerous design and implementation techniques not found in other professional references, including original techniques for : - Representing the problem in a mathematical model - Analysing the performance of the GPS sensor as a function of model parameters - Implementing the mechanization equations in numerically stable algorithms - Assessing computation requirements - Testing the validity of results - Monitoring GPS, INS, and Kalman filter performance in operation Note de contenu : 1 INTRODUCTION GPS and GLONASS Overview Differential and Augmented GPS Applications 2 FUNDAMENTALS OF SATELLITE AND INERTIAL NAVIGATION Navigation Systems Considered Fundamentals of Inertial Navigation Satellite Navigation Time and GPS User Position Calculations with No Errors User Velocity Calculation with No Errors Problems 3 SIGNAL CHARACTERISTICS AND INFORMATION EXTRACTION Mathematical Signal Waveform Models GPS Signal Components, Purposes and Properties Signal Power Levels Signal Acquisition and Tracking Extraction of Information for Navigation Solution Theoretical Considerations in Pseudorange and Frequency Estimation Modernization of GPS GPS Satellite Position Calculations Problems 4 RECEIVER AND ANTENNA DESIGN Receiver Architecture Receiver Design Choices Antenna Design Problems 5 GPS DATA ERRORS Selective Availability Errors Ionospheric Propagation Errors Tropospheric Propagation Errors The Multipath Problem How Multipath Causes Ranging Errors Methods of Multipath Mitigation Theoretical Limits for Multipath Mitigation Ephemeris Data Errors Onboard Clock Errors Receiver Clock Errors Error Budgets Problems 6 INERTIAL NAVIGATION Background Inertial Sensors Navigation Coordinates System Implementations SystemLevel Error Models 7 KALMAN FILTER BASICS Introduction State and Covariance Correction State and Covariance Prediction Summary of Kalman Filter Equations Accommodating Correlated Noise Nonlinear and Adaptive Implementations KalmanBucy Filter GPS Receiver Examples Problems 8 KALMAN FILTER ENGINEERING More Stable Implementation Methods Implementation Requirements Kalman Filter Monitoring SchmidtKalman Suboptimal Filtering Covariance Analysis GPS/INS Integration Architectures Problems 9 DIFFERENTIAL GPS Introduction LAWPS. WADGPS, and WAAS GEO Uplink Subsystem (GUS) GEO Uplink Subsystem (GUS) Clock Steering Algorithms GEO Orbit Determination Problems Numéro de notice : 12781 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Manuel de cours Accessibilité hors numérique : Non accessible via le SUDOC Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=54784