Particle Filters for Random Set Models

  • Branko Ristic

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Branko Ristic
    Pages 1-4
  3. Branko Ristic
    Pages 5-28
  4. Branko Ristic
    Pages 53-84
  5. Branko Ristic
    Pages 121-143
  6. Branko Ristic
    Pages 145-172
  7. Back Matter
    Pages 173-174

About this book


“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.


Bayesian Estimation Bernoulli Filter Filtering Algorithms Monte Carlo Statistical Method Multi-target Filter Particle Filters Random-set Based Filters Stochastic Filtering

Authors and affiliations

  • Branko Ristic
    • 1
  1. 1.DSTOPort MelbourneAustralia

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Engineering
  • Print ISBN 978-1-4614-6315-3
  • Online ISBN 978-1-4614-6316-0
  • Buy this book on publisher's site
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