  In this paper we present a general solution for multi-target tracking with
superpositional measurements. Measurements that are functions of the sum of the
contributions of the targets present in the surveillance area are called
superpositional measurements. We base our modelling on Labeled Random Finite
Set (RFS) in order to jointly estimate the number of targets and their
trajectories. This modelling leads to a labeled version of Mahler's
multi-target Bayes filter. However, a straightforward implementation of this
tracker using Sequential Monte Carlo (SMC) methods is not feasible due to the
difficulties of sampling in high dimensional spaces. We propose an efficient
multi-target sampling strategy based on Superpositional Approximate CPHD
(SA-CPHD) filter and the recently introduced Labeled Multi-Bernoulli (LMB) and
Vo-Vo densities. The applicability of the proposed approach is verified through
simulation in a challenging radar application with closely spaced targets and
low signal-to-noise ratio.
