Citation
Thomas Schon. "DREAMS tutorial: The particle filter". Talk or presentation, 20, February, 2013.

Abstract
The particle filter provides a solution to the state inference problem in nonlinear dynamical systems. This problem is indeed interesting in its own right, but it also shows up as a sub-problem in many relevant areas, such as for example sensor fusion and nonlinear system identification. The aim of this tutorial is to provide you with sufficient knowledge about the particle filter to allow you to start implementing particle filters on your own. We will start out by providing a brief introduction to probabilistic modeling of dynamical systems in order to be able to clearly define the nonlinear state inference problem under consideration. The next step is to briefly introduce two basic sampling methods, rejection sampling and importance sampling. The latter is then exploited to derive a first working particle filter. The particle filter can be interpreted as a particular member of a general class of algorithms referred to as sequential Monte Carlo (SMC). This relationship is explored in some detail in order to provide additional understanding. The particle filtering theory has developed at an increasing rate over the last two decades and it is used more and more in solving various applied problems. During this tutorial I focus on the method and to some extent on the underlying theory. Hence, I will not show any real world examples, I save them for my seminar on Thursday, where I will show how the particle filter has been instrumental in solving various nontrivial localization problems.

Electronic downloads

• dreams-tutorial-schon-2013-feb.pdf · application/pdf · 4081 kbytes

• HTML

  Thomas Schon. <a
  href="http://chess.eecs.berkeley.edu/pubs/963.html"
  ><i>DREAMS tutorial: The particle
  filter</i></a>, Talk or presentation,  20,
  February, 2013.

• Plain text

  Thomas Schon. "DREAMS tutorial: The particle
  filter". Talk or presentation,  20, February, 2013.

• BibTeX

  @presentation{Schon13_DREAMSTutorialParticleFilter,
      author = {Thomas Schon},
      title = {DREAMS tutorial: The particle filter},
      day = {20},
      month = {February},
      year = {2013},
      abstract = {The particle filter provides a solution to the
                state inference problem in nonlinear dynamical
                systems. This problem is indeed interesting in its
                own right, but it also shows up as a sub-problem
                in many relevant areas, such as for example sensor
                fusion and nonlinear system identification. The
                aim of this tutorial is to provide you with
                sufficient knowledge about the particle filter to
                allow you to start implementing particle filters
                on your own. We will start out by providing a
                brief introduction to probabilistic modeling of
                dynamical systems in order to be able to clearly
                define the nonlinear state inference problem under
                consideration. The next step is to briefly
                introduce two basic sampling methods, rejection
                sampling and importance sampling. The latter is
                then exploited to derive a first working particle
                filter. The particle filter can be interpreted as
                a particular member of a general class of
                algorithms referred to as sequential Monte Carlo
                (SMC). This relationship is explored in some
                detail in order to provide additional
                understanding. The particle filtering theory has
                developed at an increasing rate over the last two
                decades and it is used more and more in solving
                various applied problems. During this tutorial I
                focus on the method and to some extent on the
                underlying theory. Hence, I will not show any real
                world examples, I save them for my seminar on
                Thursday, where I will show how the particle
                filter has been instrumental in solving various
                nontrivial localization problems.},
      URL = {http://chess.eecs.berkeley.edu/pubs/963.html}
  }
  

Posted by David Broman on 20 Feb 2013.
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