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Box-particle intensity filter

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date15/05/2012
Host publicationData Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET
Number of pages6
Original languageEnglish

Conference

ConferenceThe 9th IET Data Fusion & Target Tracking Conference (DF & TT'2012). Algorithms & Applications
CountryUnited Kingdom
Period16/05/1217/05/12

Conference

ConferenceThe 9th IET Data Fusion & Target Tracking Conference (DF & TT'2012). Algorithms & Applications
CountryUnited Kingdom
Period16/05/1217/05/12

Abstract

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.