Consensus Labeled Random Finite Set Filtering for Distributed Multi-Object Tracking
For multi-sensor networks, this work provides a distributed solution to multi-object tracking that is scalable and computationally efficient, addressing the challenge of heterogeneous and geographically dispersed nodes.
This paper proposes two novel consensus tracking filters based on labeled Random Finite Sets for distributed multi-object tracking over heterogeneous networks, achieving fully distributed, scalable, and computationally efficient solutions validated through simulations.
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 $δ$-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.