Particle flow superpositional GLMB filter

In this paper we propose a Superpositional Marginalized delta-GLMB (SM delta-GLMB) filter for multi-target tracking and we provide bootstrap and particle flow particle filter implementations. Particle filter implementations of the marginalized delta-GLMB filter are computationally demanding. As a first contribution we show that for the specific case of superpositional observation models, a reduced complexity update step can be achieved by employing a superpositional change of variables. The resulting SM delta-GLMB filter can be readily implemented using the unscented Kalman filter or particle filtering methods. As a second contribution, we employ particle flow to produce a measurement-driven importance distribution that serves as a proposal in the SM delta-GLMB particle filter. In high-dimensional state systems or for highly-informative observations the generic particle filter often suffers from weight degeneracy or otherwise requires a prohibitively large number of particles. Particle flow avoids particle weight degeneracy by guiding particles to regions where the posterior is significant. Numerical simulations showcase the reduced complexity and improved performance of the bootstrap SM delta-GLMB filter with respect to the bootstrap M delta-GLMB filter. The particle flow SM delta-GLMB filter further improves the accuracy of track estimates for highly informative measurements.