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

Consensus Labeled Random Finite Set Filtering for Distributed Multi-Object Tracking