06179cam a2200769Mi 4500 ocn794663337 OCoLC 20171114092703.0 m o d cr |n|---||||| 120604s2012 xx om 000 0 eng d 9781118287835 (electronic bk.) 1118287835 (electronic bk.) 9781118287798 (electronic bk.) 1118287797 (electronic bk.) 0470621702 9780470621707 9781118287804 1118287800 9786613664174 EB00063293 Recorded Books AU@ 000049789633 AU@ 000052281749 DEBBG BV041430636 DEBSZ 398264317 DEBSZ 431085420 NZ1 14690891 NZ1 15340805 (OCoLC)794663337 (OCoLC)798710560 (OCoLC)805829743 (OCoLC)846954587 (OCoLC)864909775 10.1002/9781118287798 Wiley InterScience http://www3.interscience.wiley.com EBLCP eng pn EBLCP OCLCQ N$T DG1 OCLCO YDXCP CDX OCLCQ OCLCO OCLCQ OHI UMI DEBSZ OCLCQ RECBK OCLCF COO OCLCQ DG1 MAIN QA279.5 .H38 2012 MAT 029010 bisacsh 519.5/42 519.542 MAT029010 bisacsh Haug, Anton J., 1941- Bayesian estimation and tracking : a practical guide / [electronic resource] Hoboken : John Wiley & Sons, 2012. 1 online resource (523 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier References; Part II: The Gaussian Assumption: A Family of Kalman Filter Estimators; Chapter 5: The Gaussian Noise Case: Multidimensional Integration of Gaussian-Weighted Distributions; 5.1 Summary of Important Results From Chapter 3; 5.2 Derivation of the Kalman Filter Correction (Update) Equations Revisited; 5.3 The General Bayesian Point Prediction Integrals for Gaussian Densities; References; Chapter 6: The Linear Class of Kalman Filters; 6.1 Linear Dynamic Models; 6.2 Linear Observation Models; 6.3 The Linear Kalman Filter; 6.4 Application of the LKF to DIFAR Buoy Bearing Estimation. References; Chapter 7: The Analytical Linearization Class of Kalman Filters: The Extended Kalman Filter; 7.1 One-Dimensional Consideration; 7.2 Multidimensional Consideration; 7.3 An Alternate Derivation of the Multidimensional Covariance Prediction Equations; 7.4 Application of the EKF to the DIFAR Ship Tracking Case Study; References; Chapter 8: The Sigma Point Class: The Finite Difference Kalman Filter; 8.1 One-Dimensional Finite Difference Kalman Filter; 8.2 Multidimensional Finite Difference Kalman Filters. 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-world 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. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking. Print version record. Bayesian statistical decision theory. Automatic tracking Mathematics. Estimation theory. Mathematics. Bayesian statistical decision theory. Automatic tracking Mathematics. Estimation theory. MATHEMATICS Probability & Statistics Bayesian Analysis. bisacsh Mathematics. fast (OCoLC)fst01012163 Bayesian statistical decision theory. local Automatic tracking / Mathematics. local Estimation theory. local Electronic books. Print version: Haug, Anton J., 1941- Bayesian Estimation and Tracking : A Practical Guide. Hoboken : John Wiley & Sons, ©2012 9780470621707 http://onlinelibrary.wiley.com/book/10.1002/9781118287798 Wiley Online Library ddc BK 205939 205939