Particle Filters for Random Set Models, 1st Edition

  • Published By:
  • ISBN-10: 1461463165
  • ISBN-13: 9781461463160
  • DDC: 519.2
  • Grade Level Range: College Freshman - College Senior
  • 174 Pages | eBook
  • Original Copyright 2013 | Published/Released June 2014
  • This publication's content originally published in print form: 2013

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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.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
1: Introduction.
2: Background.
3: Applications Involving Non-standard Measurements.
4: Multi-Object Particle Filters.
5: Sensor Control for Random Set Based Particle Filters.
6: Multi-Target Tracking.
7: Advanced Topics.