  We consider the challenging problem of tracking multiple objects using a
distributed network of sensors. In the pragmatic settings of a limited field of
view (FoV) sensors, computing and communication resources of nodes, we develop
a novel distributed multi-target algorithm that fuses local multi-object states
instead of local multi-object densities. This algorithm uses a novel label
consensus approach that reduces label inconsistency, caused by movements of
objects from one node's limited FoV to another. To accomplish this, we
formalise the concept of label consistency and determine a sufficient condition
to achieve it. The proposed algorithm is i) fast and requires significantly
less processing time than fusion methods using multi-object filtering
densities, and ii) achieves better tracking accuracy by considering tracking
errors measured by the Optimal Sub-Pattern Assignment (OSPA) metric over
several scans rather than a single scan. Numerical experiments demonstrate the
real-time capability of our proposed solution, in computational efficiency and
accuracy compared to state-of-the-art solutions in challenging scenarios.
