Traditional space cataloguing approaches first combine consecutive measurements into "tracklets" and then associate these tracklets definitively to known objects, before updating the orbits accordingly. In contrast, multi-object tracking (MOT) methods consider multiple measurement association hypotheses simultaneously, but typically discard the pre-formed tracklets and avoid conclusive measurement-to-object assignments. This thesis presents a flexible MOT library based on finite set statistics (FISST) and proposes a modified multi-object filter that fully leverages the FISST framework while incorporating prior knowledge from existing tracklets. Additionally, a robust method is introduced to extract the most probable measurement assignments directly from the filtering recursion, enabling targeted single-object post-processing. The new tracklet filter is demonstrated to effectively discover and maintain state estimates for objects in low Earth orbit and geosynchronous orbit, using sparse optical observations from both ground-based and space-based telescopes with diverse pointing strategies.