Particle Filters for Random Set Models

Branko Ristic

ISBN 10: 148998884X / ISBN 13: 9781489988843

Published by Springer New York, Springer New York Mai 2015, 2015

About this Item

Neuware -This bookdiscusses 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 188 pp. Englisch.

Seller Inventory # 9781489988843

Bibliographic Details

• Title: Particle Filters for Random Set Models
• Publisher: Springer New York, Springer New York Mai 2015
• Publication Date: 2015
• Language: English
• ISBN 10: 148998884X
• ISBN 13: 9781489988843
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 11D millimeters 235 millimeters width by 155 millimeters height by 11 millimeters depth
• Weight: 295 grams

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