  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.
