  This paper addresses distributed multi-object tracking over a network of
heterogeneous and geographically dispersed nodes with sensing, communication
and processing capabilities. The main contribution is an approach to
distributed multi-object estimation based on labeled Random Finite Sets (RFSs)
and dynamic Bayesian inference, which enables the development of two novel
consensus tracking filters, namely a Consensus Marginalized
$\delta$-Generalized Labeled Multi-Bernoulli and Consensus Labeled
Multi-Bernoulli tracking filter. The proposed algorithms provide fully
distributed, scalable and computationally efficient solutions for multi-object
tracking. Simulation experiments via Gaussian mixture implementations confirm
the effectiveness of the proposed approach on challenging scenarios.
