Tracking targets with multiple measurements per scan

The task of tracking targets, that generate more than one measurement per scan appears in several applications such as extended object and group tracking. There, the target (or group) extend implies that muliple measurements, drawn according to a spatial probability distribution, are measured per sensor-scan. However, applications exist where targets generate several measurements per sensor-scan, which are not geometrically correlated according to a distribution in the measurement space. An example for such an application is Blind Mobile Localization, which is the passive non-cooperative localization and tracking of mobile terminals in urban scenarios. In this paper a Probability Hypothesis Density filter for general models of target-generated measurements is applied to track targets with multiple measurements per scan, where the measurements do not necessarily have to be spatially related in the measurement space. Furthermore, the problem of numerical feasibility is identified and two ways of approximating the update equation of the generalized Probability Hypothesis Density filter are proposed. Finally, a sequential Monte Carlo-implementation of the generalized Probability Hypothesis Density filter with a Poisson clutter model is used for a numerical evaluation.