  Consider the problem of tracking a set of moving targets. Apart from the
tracking result, it is often important to know where the tracking fails, either
to steer sensors to that part of the state-space, or to inform a human operator
about the status and quality of the obtained information. An intuitive quality
measure is the correlation between two tracking results based on uncorrelated
observations. In the case of Bayesian trackers such a correlation measure could
be the Kullback-Leibler difference.
  We focus on a scenario with a large number of military units moving in some
terrain. The units are observed by several types of sensors and "meta-sensors"
with force aggregation capabilities. The sensors register units of different
size. Two separate multi-target probability hypothesis density (PHD) particle
filters are used to track some type of units (e.g., companies) and their
sub-units (e.g., platoons), respectively, based on observations of units of
those sizes. Each observation is used in one filter only.
  Although the state-space may well be the same in both filters, the posterior
PHD distributions are not directly comparable -- one unit might correspond to
three or four spatially distributed sub-units. Therefore, we introduce a
mapping function between distributions for different unit size, based on
doctrine knowledge of unit configuration.
  The mapped distributions can now be compared -- locally or globally -- using
some measure, which gives the correlation between two PHD distributions in a
bounded volume of the state-space. To locate areas where the tracking fails, a
discretized quality map of the state-space can be generated by applying the
measure locally to different parts of the space.
