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|Additional Physical Format:||Print version:
Haug, Anton J., 1941-
Bayesian Estimation and Tracking : A Practical Guide.
Hoboken : John Wiley & Sons, ©2012
|Material Type:||Document, Internet resource|
|Document Type:||Internet Resource, Computer File|
|All Authors / Contributors:||
Anton J Haug
|ISBN:||9781118287835 1118287835 9781118287798 1118287797 0470621702 9780470621707 9781118287804 1118287800|
|Notes:||10.4 The Spherical Simplex Kalman Filter.|
|Description:||1 online resource (523 pages)|
|Contents:||Cover; Title Page; Copyright; Dedication; Preface; Acknowledgments; List of Figures; List of Tables; Part I: Preliminaries; Chapter 1: Introduction; 1.1 Bayesian Inference; 1.2 Bayesian Hierarchy of Estimation Methods; 1.3 Scope of this Text; 1.4 Modeling and Simulation with Matlab®; References; Chapter 2: Preliminary Mathematical Concepts; 2.1 A Very Brief Overview of Matrix Linear Algebra; 2.2 Vector Point Generators; 2.3 Approximating Nonlinear Multidimensional Functions with Multidimensional Arguments; 2.4 Overview of Multivariate Statistics; References. Chapter 3: General Concepts of Bayesian Estimation; 3.1 Bayesian Estimation; 3.2 Point Estimators; 3.3 Introduction to Recursive Bayesian Filtering of Probability Density Functions; 3.4 Introduction to Recursive Bayesian Estimation of the State Mean and Covariance; 3.5 Discussion of General Estimation Methods; References; Chapter 4: Case Studies: Preliminary Discussions; 4.1 The Overall Simulation/Estimation/Evaluation Process; 4.2 A Scenario Simulator for Tracking a Constant Velocity Target Through a DIFAR Buoy Field; 4.3 DIFAR Buoy Signal Processing; 4.4 The DIFAR Likelihood Function. 8.3 An Alternate Derivation of the Multidimensional Finite Difference Covariance Prediction Equations; References; Chapter 9: The Sigma Point Class: The Unscented Kalman Filter; 9.1 Introduction to Monomial Cubature Integration Rules; 9.2 The Unscented Kalman Filter; 9.3 Application of the UKF to the DIFAR Ship Tracking Case Study; References; Chapter 10: The Sigma Point Class: The Spherical Simplex Kalman Filter; 10.1 One-Dimensional Spherical Simplex Sigma Points; 10.2 Two-Dimensional Spherical Simplex Sigma Points; 10.3 Higher Dimensional Spherical Simplex Sigma Points.|
A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation.
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