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

The 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 information-theoretic approach to distributed 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 (CM$\delta$GLMB) and consensus Labeled Multi-Bernoulli (CLMB) 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.