PMHT for fused tracking

Fusing data together for target tracking is a complex problem. There are two key steps. First, the raw observations must be associated with existing tracks or used to form new tracks. Once the association has been done, then the tracks can be updated and filtered with the new data. The updating and filtering is usually the easier of the two parts and it is the association that can lead to most of the complexity in target tracking. When associating data (either measurements or tracks or both) with existing tracks, the separation between the tracks is critical to how difficult the association decisions will be. If the tracks are widely separated then the association decisions can be relatively easy. On the other hand, when the tracks are closely spaced the association decisions can be very difficult or nearly impossible. When the tracks or measurements are in three dimensions (such as with active sensors) the association can be accomplished in all three dimension thus making an easier distinction of targets that may be very close in two dimensions, but distant in the third dimension. However, when there are only two dimensions (as for passive sensors) observed by a sensor, targets that are widely separated may appear to be very close or even unresolved. In this paper, we will discuss the issues involved with applying the Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm to fusing either measurements or tracks from passive sensors.