  When tracking a large number of targets, it is often computationally
expensive to represent the full joint distribution over target states. In cases
where the targets move independently, each target can instead be tracked with a
separate filter. However, this leads to a model-data association problem.
Another approach to solve the problem with computational complexity is to track
only the first moment of the joint distribution, the probability hypothesis
density (PHD). The integral of this distribution over any area S is the
expected number of targets within S. Since no record of object identity is
kept, the model-data association problem is avoided.
  The contribution of this paper is a particle filter implementation of the PHD
filter mentioned above. This PHD particle filter is applied to tracking of
multiple vehicles in terrain, a non-linear tracking problem. Experiments show
that the filter can track a changing number of vehicles robustly, achieving
near-real-time performance.
