Robust Report Level Cluster-to-Track Fusion

TL;DR

This paper presents a robust report-to-track fusion method using Dempster-Shafer clustering with Potts spin neural networks, enabling efficient and accurate association of reports to tracks over time.

Contribution

It introduces a novel report-level tracking approach that fuses reports into existing tracks through continuous reclustering, improving robustness and computational efficiency.

Findings

Faster than clustering all reports at once

Improves report-to-track association accuracy

Handles evolving report data effectively

Abstract

In this paper we develop a method for report level tracking based on Dempster-Shafer clustering using Potts spin neural networks where clusters of incoming reports are gradually fused into existing tracks, one cluster for each track. Incoming reports are put into a cluster and continuous reclustering of older reports is made in order to obtain maximum association fit within the cluster and towards the track. Over time, the oldest reports of the cluster leave the cluster for the fixed track at the same rate as new incoming reports are put into it. Fusing reports to existing tracks in this fashion allows us to take account of both existing tracks and the probable future of each track, as represented by younger reports within the corresponding cluster. This gives us a robust report-to-track association. Compared to clustering of all available reports this approach is computationally faster and has a better report-to-track association than simple step-by-step association.