Box-particle intensity filter

M. Schikora, A. Gning, L. Mihaylova, D. Cremers, W. Koch, R. Streit

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


This paper develops a novel approach for multi-target tracking, called box-particle intensity filter (box-iFilter). The approach is able to cope with unknown clutter, false alarms and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. The box-iFilter reduces the number of particles significantly, which improves the runtime considerably. The low particle number enables this approach to be used for distributed computing. A box-particle is a random sample that occupies a small and controllable rectangular region of non-zero volume. Manipulation of boxes utilizes the methods from the field of interval analysis. Our studies suggest that the box-iFilter reaches an accuracy similar to a sequential Monte Carlo (SMC) iFilter but with much less computational costs.

Original languageEnglish
Title of host publication9th IET Data Fusion and Target Tracking Conference, DF and TT 2012
Subtitle of host publicationAlgorithms and Applications
Number of pages1
Edition595 CP
StatePublished - 2012
Event9th IET Data Fusion and Target Tracking Conference: Algorithms and Applications, DF and TT 2012 - London, United Kingdom
Duration: 16 May 201217 May 2012

Publication series

NameIET Conference Publications
Number595 CP


Conference9th IET Data Fusion and Target Tracking Conference: Algorithms and Applications, DF and TT 2012
Country/TerritoryUnited Kingdom


  • Box particle filters
  • Intensity filter
  • Interval measurements
  • Multi-target tracking
  • Poisson point processes


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