Multi-target particle filtering for the probability hypothesis density

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 as- sociation problem. Another approach to solve the prob- lem with computational complexity is to track only the first moment of the joint distribution, the probability hypothe- sis 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 imple- mentation 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 ro- bustly, achieving near-real-time performance.