4 research outputs found
Robust Report Level Cluster-to-Track Fusion
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.Comment: 6 pages, 5 figure
Managing inconsistent intelligence
In this paper we demonstrate that it is possible to manage intelligence in constant time as a pre-process to information fusion through a series of processes dealing with issues such as clustering reports, ranking reports with respect to importance, extraction of prototypes from clusters and immediate classification of newly arriving intelligence reports. These methods are used when intelligence reports arrive which concerns different events which should be handled independently, when it is not known a priori to which event each intelligence report is related. We use clustering that runs as a back-end process to partition the intelligence into subsets representing the events, and in parallel, a fast classification that runs as a front-end process in order to put the newly arriving intelligence into its correct information fusion process