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006 m o d
007 cr |n|---|||||
008 120604s2012 xx om 000 0 eng d
040 _aEBLCP
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019 _a798710560
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020 _a9781118287835
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020 _a1118287835
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020 _a9781118287798
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020 _a1118287797
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020 _a9780470621707
020 _a9781118287804
020 _a1118287800
024 8 _a9786613664174
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035 _a(OCoLC)794663337
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_z(OCoLC)805829743
_z(OCoLC)846954587
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037 _a10.1002/9781118287798
_bWiley InterScience
_nhttp://www3.interscience.wiley.com
050 4 _aQA279.5 .H38 2012
072 7 _aMAT
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082 0 4 _a519.5/42
_a519.542
084 _aMAT029010
_2bisacsh
049 _aMAIN
100 1 _aHaug, Anton J.,
_d1941-
245 1 0 _aBayesian estimation and tracking :
_ba practical guide.
260 _aHoboken :
_bJohn Wiley & Sons,
_c2012.
300 _a1 online resource (523 pages)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
505 0 _aCover; 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.
505 8 _aChapter 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.
504 _aReferences; 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.
504 _aReferences; 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.
505 8 _a8.3 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.
520 _aA 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.
588 0 _aPrint version record.
650 0 _aBayesian statistical decision theory.
650 0 _aAutomatic tracking
_xMathematics.
650 0 _aEstimation theory.
650 4 _aMathematics.
650 4 _aBayesian statistical decision theory.
650 4 _aAutomatic tracking
_xMathematics.
650 4 _aEstimation theory.
650 7 _aMATHEMATICS
_xProbability & Statistics
_xBayesian Analysis.
_2bisacsh
650 7 _aMathematics.
_2fast
_0(OCoLC)fst01012163
650 7 _aBayesian statistical decision theory.
_2local
650 7 _aAutomatic tracking / Mathematics.
_2local
650 7 _aEstimation theory.
_2local
655 4 _aElectronic books.
776 0 8 _iPrint version:
_aHaug, Anton J., 1941-
_tBayesian Estimation and Tracking : A Practical Guide.
_dHoboken : John Wiley & Sons, ©2012
_z9780470621707
856 4 0 _uhttp://dx.doi.org/10.1002/9781118287798
_zWiley Online Library
994 _a92
_bDG1
999 _c19476
_d19435