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SUMMARY:Stochastic Geometry in Dynamic State Estimation - Professor Ba-Ngu
  Vo\, University of Western Australia
DTSTART:20130719T130000Z
DTEND:20130719T143000Z
UID:TALK46254@talks.cam.ac.uk
CONTACT:Claire Whitaker
DESCRIPTION:Abstract: The last decade has witnessed exciting developments 
 in multi-object state estimation with the introduction of stochastic geome
 try to the field. Stochastic geometry-the marriage between geometry and pr
 obability-is a mathematical discipline that deals with random spatial patt
 erns. The history of stochastic geometry traces back to the problem of Buf
 fon's needle and has long been used by  statisticians in many diverse appl
 ications including astronomy\,\nparticle physics\, biology\, sampling theo
 ry\, stereology\, etc. Since 2003\, Mahler's seminal work on the random fi
 nite set approach to multi-object filtering\, which culminated in the prob
 ability hypothesis density (PHD) filter\, has continued to attract substan
 tial interests from academia and industry alike. This seminar presents an 
 overview of the random finite set paradigm to dynamic state estimation and
  outlines recent developments beyond the PHD filters  as well as applicati
 ons in areas such as sensor scheduling\, computer vision\, and field robot
 ics.\n
LOCATION:LR6\, Engineering\, Department of
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