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BEGIN:VEVENT
UID:873@lincs.fr
DTSTART;TZID=Europe/Paris:20250207T110000
DTEND;TZID=Europe/Paris:20250207T120000
DTSTAMP:20250214T142223Z
URL:https://www.lincs.fr/events/particle-filter-based-statistical-inferenc
 e-for-positioning-and-tracking-applications/
SUMMARY:Particle filter based statistical inference for positioning and
 tracking applications
DESCRIPTION:The lecture will provide a comprehensive summary of statistical
 inference using particle filters for positioning and tracking applications.
 The lecture will cover the motivation behind developing such frameworks\,
 the underlying motion and measurement models\, assumptions\, and the
 Bayesian approach towards solving such an estimation problem. The talk will
 also highlight the importance of linearity\, Gaussian\, and Markovian
 assumptions\, enabling a tractable solution framework also popularly termed
 as Kalman filter (KF). Insights will be provided into discrete time and
 continuous time KFs\, including the steady state stability conditions.
 Further\, to deal with non-linear systems\, the talk with discuss the
 linearization based extended Kalman filter.\n\nReference: Niclas Bergman\,
 "Recursive Bayesian Estimation Navigation and Tracking Applications"\,
 Department of Electrical Engineering Linkoping University\, Sweden
CATEGORIES:Network Theory,Working Group,Youtube
LOCATION:Amphi 2\, 19 place Marguerite Perey\, Palaiseau\, France
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 Palaiseau\, France;X-APPLE-RADIUS=100;X-TITLE=Amphi 2:geo:0,0
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TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
BEGIN:STANDARD
DTSTART:20241027T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
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