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The Gaussian mixture-cardinalized probability hypothesis density (GM-CPHD) tracker was applied to three real and simulated multistatic sonar data sets. The goal was to test the versatility of the tracker on data of increasing difficulty. The first two data sets presented minor challenges for the tracker and in our opinion demonstrate that it is, indeed, a tracking paradigm that is ready for “prime time.” The last data set was considerably more challenging. Without some means to use data from multiple sensors success would be in doubt. However, since a practicable multisensor form of the probability hypothesis density (PHD) filter is still unclear, predetection fusion (contact sifting) was considered a necessary first step before tracking. On all the data sets investigated, the GM-CPHD proved to be easily adaptable (e.g., to low probability of detection, large number of sensors), discovered all the targets and generated satisfactory tracks for them. Plots of the tracks obtained and the associated metrics of performance are provided.