Particle Filters for Random Set Models (PDF)
(Sprache: Englisch)
"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...
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"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.
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.
Autoren-Porträt von Branko Ristic
Branko Ristic is at the Defence Science and Technology Organisation, AustraliaDefence Science and Technology Organisation, Australia
Bibliographische Angaben
- Autor: Branko Ristic
- 2013, 2013, 174 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 1461463165
- ISBN-13: 9781461463160
- Erscheinungsdatum: 15.04.2013
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
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- Dateiformat: PDF
- Größe: 5.76 MB
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Sprache:
Englisch
Pressezitat
From the book reviews:“The book realizes a happy union between theory and practice. Of high interest are the Algorithms for which their pseudo-codes are presented. We think we are faced with an excellent book that will have a great success and audience between those interested for new approaches in filtering theory.” (Dumitru Stanomir, zbMATH 1306.93002, 2015)
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