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  xmlns:dcterms="http://purl.org/dc/terms/"><dc:Title>Bayesian estimation and tracking : a practical guide / [electronic resource]</dc:Title>
<dc:Creator>Haug, Anton J., 1941-</dc:Creator>
<dc:Subject>Bayesian statistical decision theory.</dc:Subject>
<dc:Subject>Automatic tracking Mathematics.</dc:Subject>
<dc:Subject>Estimation theory.</dc:Subject>
<dc:Subject>QA279.5 .H38 2012</dc:Subject>
<dc:Subject>519.5/42 519.542</dc:Subject>
<dc:Description>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.</dc:Description>
<dc:Description>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.</dc:Description>
<dc:Description>Print version record.</dc:Description>
<dc:Description>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.</dc:Description>
<dc:Publisher>Hoboken : John Wiley & Sons,</dc:Publisher>
<dc:Date>2012.</dc:Date>
<dc:Date>2012.</dc:Date>
<dc:Date>2012</dc:Date>
<dc:Type>Text</dc:Type>
<dc:Format>1 online resource (523 pages)</dc:Format>
<dc:Identifier>http://onlinelibrary.wiley.com/book/10.1002/9781118287798</dc:Identifier>
<dc:Language>eng</dc:Language>
<dc:Relation>Bayesian Estimation and Tracking : A Practical Guide.</dc:Relation>
<dc:Relation>Bayesian Estimation and Tracking : A Practical Guide.</dc:Relation>

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