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In this paper we propose a Superpositional Marginalized δ-GLMB (SMδ-GLMB) filter for multi-target tracking and we provide bootstrap and particle flow particle filter implementations. Particle filter implementations of the marginalized δ-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 SMδ-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 SMδ-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 …