|Title||A structured mean field approach for tracking a coordinated group|
|Presenter||Roslyn Anne Lau (DST Group)|
|Author(s)||Roslyn A. Lau and Jason L. Williams|
|Session||Mathematical Physics and Industrial Mathematics|
Multiple target tracking answers questions such as “How many targets are there?”, “Where are they now?”, and “Where are they going?”. This requires the processing of large amounts of data arriving from one or more sensors in a computationally efficient manner. One of the common assumptions of multiple target tracking is that targets travel independently; however, there are several cases where targets travel in a group. For example, a convoy of vehicles may travel on a road, a fleet of ships may sail across the ocean, and a group of planes may travel in formation. Tracking a coordinated group of targets using radar detections is challenging because filters can incorrectly produce tracks that switch identities (track swaps), or tracks can incorrectly converge to the same estimate (track coalescence). In this paper, we present a unified model to track a single group of targets in which the number of targets is varying and unknown. Specifically, we extend the virtual leader model to formulate the group of targets and apply structured mean field approximations to produce a computationally efficient algorithm. By using the dependent motion model and structured mean field approach, we are able to reduce track swaps and coalescence. Results for simulated data show the algorithm to be accurate for various values of probability of detection, false alarm rate, and measurement noise. Future extensions include tracking multiple groups of targets and further reducing the computational complexity.
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