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DTSTAMP:20250404T145702Z
UID:6FFA7439-6021-470D-B2B4-CBC0D8CFDABA
DTSTART;TZID=Europe/Warsaw:20250404T100000
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DESCRIPTION:The increasing trend towards connected sensors (“internet of 
 things” and “ubiquitous computing”) derive a demand for powerful dis
 tributed estimation methodologies. In tracking applications\, the “Distr
 ibuted Kalman Filter” (DKF) provides an optimal solution under certain c
 onditions. The optimal solution in terms of the estimation accuracy is als
 o achieved by a centralized fusion algorithm which receives either all ass
 ociated measurements or so-called “tracklets”. However\, this scheme n
 eeds the result of each update step for the optimal solution whereas the D
 KF works at arbitrary communication rates since the calculation is complet
 ely distributed. Two more recent methodologies are based on the “Accumul
 ated State Densities” (ASD) which augment the states from multiple time 
 instants. In practical applications\, tracklet fusion based on the equival
 ent measurement often achieves reliable results even if full communication
  is not available. The limitations and robustness of the tracklet fusion w
 ill be discussed.\n\nAt first\, the Distinguished Lecture will explain the
  origin of the challenges in distributed tracking. Then\, possible solutio
 ns to them are derived and illuminated. In particular\, algorithms will be
  provided for each presented solution.\n\nThe list of topics includes: Sho
 rt introduction to target tracking\, Tracklet Fusion\, Exact Fusion with c
 ross-covariances\, Naive Fusion\, Federated Fusion\, Decentralized Fusion 
 (Consensus Kalman Filter)\, Distributed Kalman Filter (DKF)\, Debiasing fo
 r the DKF\, Distributed ASD Fusion\, Augmented State Tracklet Fusion.\n\nS
 peaker(s): Felix Govaers\n\nRoom: 229\, Bldg: Faculty of Electronics and I
 nformation Technology\, Nowowiejska 15/19\, Warsaw University of Technolog
 y\, Warsaw\, Mazowieckie\, Poland\, 00-665
LOCATION:Room: 229\, Bldg: Faculty of Electronics and Information Technolog
 y\, Nowowiejska 15/19\, Warsaw University of Technology\, Warsaw\, Mazowie
 ckie\, Poland\, 00-665
ORGANIZER:mateusz.malanowski@pw.edu.pl
SEQUENCE:14
SUMMARY:An Introduction to Track-to-Track Fusion and the Distributed Kalman
  Filter
URL;VALUE=URI:https://events.vtools.ieee.org/m/476781
X-ALT-DESC:Description: <br /><p>The increasing trend towards connected sen
 sors (&ldquo\;internet of things&rdquo\; and &ldquo\;ubiquitous computing&
 rdquo\;) derive a demand for powerful distributed estimation methodologies
 . In tracking applications\, the &ldquo\;Distributed Kalman Filter&rdquo\;
  (DKF) provides an optimal solution under certain conditions. The optimal 
 solution in terms of the estimation accuracy is also achieved by a central
 ized fusion algorithm which receives either all associated measurements or
  so-called &ldquo\;tracklets&rdquo\;. However\, this scheme needs the resu
 lt of each update step for the optimal solution whereas the DKF works at a
 rbitrary communication rates since the calculation is completely distribut
 ed. Two more recent methodologies are based on the &ldquo\;Accumulated Sta
 te Densities&rdquo\; (ASD) which augment the states from multiple time ins
 tants. In practical applications\, tracklet fusion based on the equivalent
  measurement often achieves reliable results even if full communication is
  not available. The limitations and robustness of the tracklet fusion will
  be discussed.&nbsp\;</p>\n<p>At first\, the Distinguished Lecture will ex
 plain the origin of the challenges in distributed tracking. Then\, possibl
 e solutions to them are derived and illuminated. In particular\, algorithm
 s will be provided for each presented solution.&nbsp\;</p>\n<p>The list of
  topics includes: Short introduction to target tracking\, Tracklet Fusion\
 , Exact Fusion with cross-covariances\, Naive Fusion\, Federated Fusion\, 
 Decentralized Fusion (Consensus Kalman Filter)\, Distributed Kalman Filter
  (DKF)\, Debiasing for the DKF\, Distributed ASD Fusion\, Augmented State 
 Tracklet Fusion.</p>
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