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DREAMS tutorial: The particle filter
Thomas Schon

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

Citation formats  
  • 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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