Particle Filters for Random Set Models - Softcover

Ristic, Branko

Softcover

ISBN 10: 148998884X ISBN 13: 9781489988843

Verlag: Springer, 2015

Inhaltsangabe

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.

Über diesen Titel

Über die Autorin bzw. den Autor

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

Defence Science and Technology Organisation, Australia

Von der hinteren Coverseite

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.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2015
• Sprache: Englisch
• ISBN 10: 148998884X
• ISBN 13: 9781489988843
• Einband: Tapa blanda
• Anzahl der Seiten: 188
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Deutschland