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Particle Filters for Random Set Models

  • Book
  • © 2013

Overview

Authors:
  1. Branko Ristic

    1. DSTO, Port Melbourne, Australia

    View author publications

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  • Presents a hands-on engineering approach to filtering algorithms and their implementation
  • Covers a new generation of particle filters, which are applicable to a much wider class of signal processing applications
  • Includes sensor control for particle filters
  • Provides information on a number of interesting and relevant applications, which illustrate theoretical concepts and demonstrate the performance of developed particle filters

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xiv
    Download chapter PDF

  2. Introduction

    • Branko Ristic
    Pages 1-4

  3. Background

    • Branko Ristic
    Pages 5-28

  4. Applications Involving Non-standard Measurements

    • Branko Ristic
    Pages 29-51

  5. Multi-Object Particle Filters

    • Branko Ristic
    Pages 53-84

  6. Sensor Control for Random Set BasedParticle Filters

    • Branko Ristic
    Pages 85-119

  7. Multi-Target Tracking

    • Branko Ristic
    Pages 121-143

  8. Advanced Topics

    • Branko Ristic
    Pages 145-172

  9. Back Matter

    Pages 173-174
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Keywords

About this book

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.

Reviews

From the book reviews:

“The book realizes a happy union between theory and practice. Of high interest are the Algorithms for which their pseudo-codes are presented. We think we are faced with an excellent book that will have a great success and audience between those interested for new approaches in filtering theory.” (Dumitru Stanomir, zbMATH 1306.93002, 2015)

Authors and Affiliations

  • DSTO, Port Melbourne, Australia

    Branko Ristic

About the author

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

Defence Science and Technology Organisation, Australia

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