Extremal optimization for sensor report pre-processing
  We describe the recently introduced extremal optimization algorithm and apply
it to target detection and association problems arising in pre-processing for
multi-target tracking.
  Here we consider the problem of pre-processing for multiple target tracking
when the number of sensor reports received is very large and arrives in large
bursts. In this case, it is sometimes necessary to pre-process reports before
sending them to tracking modules in the fusion system. The pre-processing step
associates reports to known tracks (or initializes new tracks for reports on
objects that have not been seen before). It could also be used as a pre-process
step before clustering, e.g., in order to test how many clusters to use.
  The pre-processing is done by solving an approximate version of the original
problem. In this approximation, not all pair-wise conflicts are calculated. The
approximation relies on knowing how many such pair-wise conflicts that are
necessary to compute. To determine this, results on phase-transitions occurring
when coloring (or clustering) large random instances of a particular graph
ensemble are used.
