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

TL;DR

This paper introduces novel consensus filtering algorithms for distributed multi-object tracking using labeled Random Finite Sets, enabling scalable and efficient estimation across networked sensors.

Contribution

It develops two new consensus filters based on labeled RFSs and Bayesian inference, advancing distributed multi-object tracking methods.

Findings

Algorithms are fully distributed and scalable.

Simulation results confirm effectiveness in challenging scenarios.

Approach improves computational efficiency and accuracy.

Abstract

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.