The task of tracking targets, that generate more than one measurement per scan appears in several applications such as extended object and group tracking. In this case, the target (or group) extent implies that multiple 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, two numerical evaluations are carried out to assess sequential Monte Carlo-implementations of the generalized PHD-filter.