Schikora, Marek and Gning, Amadou and Mihaylova, Lyudmila and Cremers, Daniel and Koch, Wofgang and Streit, Roy (2012) Box-particle intensity filter. In: Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET :. UNSPECIFIED, GBR.
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.