Particle Filters for Random Set Models

Branko Ristic

ISBN 10: 148998884X / ISBN 13: 9781489988843

Edité par Springer New York, Springer New York, 2015

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9781489988843

Détails bibliographiques

• Titre: Particle Filters for Random Set Models
• Éditeur: Springer New York, Springer New York
• Date de parution: 2015
• Langue: anglais
• ISBN à 10 chiffres: 148998884X
• ISBN à 13 chiffres: 9781489988843
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 155 H x 11 P (en millimètres) 235 millimètres de largeur par 155 millimètres de hauteur par 11 millimètres de profondeur
• Poids: 295 grammes

A propos de ce titre

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.

À propos de l?auteur

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

Defence Science and Technology Organisation, Australia