Particle Filters for Random Set Models - Tapa dura

Ristic

 
9781461463153: Particle Filters for Random Set Models
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ISBN 10: 1461463157 ISBN 13: 9781461463153
Editorial: Springer, 2013

Sinopsis

This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. Although the resulting algorithms, known as particle filters, have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.

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Acerca del autor

Branko Ristic is at the Defence Science and Technology Organisation, Australia

Defence Science and Technology Organisation, Australia

De la contraportada

Particle Filters for Random Set Models presents coverage of state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. The resulting algorithms, known as particle filters, in the last decade have become one of the essential tools for stochastic filtering, with applications ranging from navigation and autonomous vehicles to bio-informatics and finance.

While particle filters have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. These recent developments have dramatically widened the scope of applications, from single to multiple appearing/disappearing objects, from precise to imprecise measurements and measurement models.

This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.

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Editorial
Springer
Año de publicación
2013
Idioma
Inglés
ISBN 10 
1461463157
ISBN 13 
9781461463153
Encuadernación
Tapa dura
Número de páginas
188
Contacto del fabricante
no disponible
Persona responsable
CMN Printpool Inh. Mathis Neumann
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Hamburg
Alemania

Otras ediciones populares con el mismo título

9781489988843: Particle Filters for Random Set Models

Edición Destacada

ISBN 10:  148998884X ISBN 13:  9781489988843
Editorial: Springer, 2015
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