Particle Filters for Random Set Models

Branko Ristic

ISBN 10: 148998884X / ISBN 13: 9781489988843

Published by Springer-Verlag New York Inc., US, 2015

About this Item

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.

Seller Inventory # LU-9781489988843

Bibliographic Details

• Title: Particle Filters for Random Set Models
• Publisher: Springer-Verlag New York Inc., US
• Publication Date: 2015
• Language: English
• ISBN 10: 148998884X
• ISBN 13: 9781489988843
• Binding: Paperback
• Edition: 2013 ed.
• Condition: New

About this title

Synopsis

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.

About the Author

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

Defence Science and Technology Organisation, Australia