Bayesian Estimation and Tracking A Practical Guide
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Form:Einzelkauf Download
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Sprache:Englisch
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eBook Format:ePUB
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Produktdetails
Format
ePUB
Kopierschutz
Ja
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
29.05.2012
Verlag
SybexSeitenzahl
400 (Printausgabe)
Dateigröße
5960 KB
Auflage
1. Auflage
Sprache
Englisch
EAN
9781118287804
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. 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, the book emphasizes the derivation of all tracking
algorithms within a Bayesian framework and describes effective
numerical methods for evaluating density-weighted integrals,
including linear and nonlinear Kalman filters for Gaussian-weighted
integrals and particle filters for non-Gaussian cases. The author
first emphasizes detailed derivations from first principles of
eeach estimation method and goes on to use illustrative and
detailed step-by-step instructions for each method that makes
coding of the tracking filter simple and easy to understand.
Case studies are employed to showcase applications of the
discussed topics. In addition, the book supplies block diagrams for
each algorithm, allowing readers to develop their own MATLAB®
toolbox of estimation methods.
Bayesian Estimation and Tracking is an excellent book for
courses on estimation and tracking methods at the graduate level.
The book also serves as a valuable reference for research
scientists, mathematicians, and engineers seeking a deeper
understanding of the topics.
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. 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, the book emphasizes the derivation of all tracking
algorithms within a Bayesian framework and describes effective
numerical methods for evaluating density-weighted integrals,
including linear and nonlinear Kalman filters for Gaussian-weighted
integrals and particle filters for non-Gaussian cases. The author
first emphasizes detailed derivations from first principles of
eeach estimation method and goes on to use illustrative and
detailed step-by-step instructions for each method that makes
coding of the tracking filter simple and easy to understand.
Case studies are employed to showcase applications of the
discussed topics. In addition, the book supplies block diagrams for
each algorithm, allowing readers to develop their own MATLAB®
toolbox of estimation methods.
Bayesian Estimation and Tracking is an excellent book for
courses on estimation and tracking methods at the graduate level.
The book also serves as a valuable reference for research
scientists, mathematicians, and engineers seeking a deeper
understanding of the topics.
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