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06174nam a2200769 4500 |
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ocn794663337 |
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OCoLC |
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20170124071855.2 |
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m o d |
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120604s2012 xx om 000 0 eng d |
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|a 798710560
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|a 10.1002/9781118287798
|b Wiley InterScience
|n http://www3.interscience.wiley.com
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|a QA279.5 .H38 2012
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|2 bisacsh
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|a MAIN
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| 100 |
1 |
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|a Haug, Anton J.,
|d 1941-
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| 245 |
1 |
0 |
|a Bayesian estimation and tracking :
|b a practical guide.
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| 264 |
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|a Hoboken :
|b John Wiley & Sons,
|c 2012.
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| 300 |
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|a 1 online resource (523 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a 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.
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|a 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.
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|a 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.
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|a 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.
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|a 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.
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|a 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.
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| 588 |
0 |
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|a Print version record.
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| 650 |
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|a Bayesian statistical decision theory.
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| 650 |
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|a Automatic tracking
|x Mathematics.
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| 650 |
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|a Estimation theory.
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| 650 |
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4 |
|a Mathematics.
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| 650 |
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4 |
|a Bayesian statistical decision theory.
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| 650 |
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4 |
|a Automatic tracking
|x Mathematics.
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| 650 |
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|a Estimation theory.
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| 650 |
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7 |
|a MATHEMATICS
|x Probability & Statistics
|x Bayesian Analysis.
|2 bisacsh
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| 650 |
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7 |
|a Mathematics.
|2 fast
|0 (OCoLC)fst01012163
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| 650 |
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|a Bayesian statistical decision theory.
|2 local
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| 650 |
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7 |
|a Automatic tracking / Mathematics.
|2 local
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| 650 |
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|a Estimation theory.
|2 local
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| 655 |
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|a Electronic books.
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| 776 |
0 |
8 |
|i Print version:
|a Haug, Anton J., 1941-
|t Bayesian Estimation and Tracking : A Practical Guide.
|d Hoboken : John Wiley & Sons, ©2012
|z 9780470621707
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| 856 |
4 |
0 |
|u https://doi.org/10.1002/9781118287798
|z Full Text via HEAL-Link
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| 994 |
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|a 92
|b DG1
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