Distributed multi-object tracking over sensor networks: a random finite set approach

TL;DR

This paper develops a distributed multi-object tracking framework over sensor networks using a random finite set approach, combining Bayesian filtering, consensus algorithms, and Kullback-Leibler averaging for scalable, efficient multi-object estimation.

Contribution

It introduces a novel distributed filtering method for multi-object tracking using labeled RFS and consensus algorithms, extending Bayesian filtering to a networked multi-object context.

Findings

Developed a scalable, fully distributed multi-object tracking filter.

Extended Kullback-Leibler averaging to labeled RFS for consensus.

Demonstrated effectiveness in tracking multiple objects with unique identities.

Abstract

The aim of the present dissertation is to address distributed tracking over a network of heterogeneous and geographically dispersed nodes (or agents) with sensing, communication and processing capabilities. Tracking is carried out in the Bayesian framework and its extension to a distributed context is made possible via an information-theoretic approach to data fusion which exploits consensus algorithms and the notion of Kullback-Leibler Average (KLA) of the Probability Density Functions (PDFs) to be fused. The first step toward distributed tracking considers a single moving object. Consensus takes place in each agent for spreading information over the network so that each node can track the object. To achieve such a goal, consensus is carried out on the local single-object posterior distribution, which is the result of local data processing, in the Bayesian setting, exploiting the last available measurement about the object. The next step is in the direction of distributed estimation of multiple moving objects. In order to model, in a rigorous and elegant way, a possibly time-varying number of objects present in a given area of interest, the Random Finite Set (RFS) formulation is adopted since it provides the notion of probability density for multi-object states that allows to directly extend existing tools in distributed estimation to multi-object tracking. The last theoretical step of the present dissertation is toward distributed filtering with the further requirement of unique object identities. To this end the labeled RFS framework is adopted as it provides a tractable approach to the multi-object Bayesian recursion. A generalization of the KLA to the labeled RFS framework, enables the development of novel consensus multi-object tracking filters which are fully distributed, scalable and computationally efficient.