  With the increasing complexity of multiple target tracking scenes, a single
sensor may not be able to effectively monitor a large number of targets.
Therefore, it is imperative to extend the single-sensor technique to
Multi-Sensor Multi-Target Tracking (MSMTT) for enhanced functionality. Typical
MSMTT methods presume complete randomness of all uncertain components, and
therefore effective solutions such as the random finite set filter and
covariance intersection method have been derived to conduct the MSMTT task.
However, the presence of epistemic uncertainty, arising from incomplete
information, is often disregarded within the context of MSMTT. This paper
develops an innovative possibility Labeled Multi-Bernoulli (LMB) Filter based
on the labeled Uncertain Finite Set (UFS) theory. The LMB filter inherits the
high robustness of the possibility generalized labeled multi-Bernoulli filter
with simplified computational complexity. The fusion of LMB UFSs is derived and
adapted to develop a robust MSMTT scheme. Simulation results corroborate the
superior performance exhibited by the proposed approach in comparison to
typical probabilistic methods.
