  The multi-target Bayes filter proposed by Mahler is a principled solution to
recursive Bayesian tracking based on RFS or FISST. The $\delta$-GLMB filter is
an exact closed form solution to the multi-target Bayes recursion which yields
joint state and label or trajectory estimates in the presence of clutter,
missed detections and association uncertainty. Due to presence of explicit data
associations in the $\delta$-GLMB filter, the number of components in the
posterior grows without bound in time. In this work we propose an efficient
approximation to the $\delta$-GLMB filter which preserves both the PHD and
cardinality distribution of the labeled posterior. This approximation also
facilitates efficient multi-sensor tracking with detection-based measurements.
Simulation results are presented to verify the proposed approach.
