This paper reviews key elements in the development of multiple-hypothesis tracking (MHT), a leading paradigm for multitarget tracking, as well as graph-based tracking (GBT), a scalable version of MHT that has proven effective in kinematic track stitching applications. We introduce a novel MHT/GBT algorithm that we denote as multi-INT GBT (MI-GBT). It provides computational benefits over classical MHT, while allowing for static components of the target state that classical GBT does not. Thus, the MI-GBT provides an effective method for multisensor feature-aided track fusion with disparate sensors. We quantify the improved performance over the MHT solution in Monte Carlo studies.