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.

The economic and holistic incentives of asteroid exploration have sparked an increased interest within the space industry. Missions like Hera and M-ARGO have taken steps towards said exploration, each with its own concept, but both using CubeSats due to the versatility they add. Thus, an interesting question arises on whether adopting a distributed, deep space system approach will reduce the costs and increase the versatility of the mission. To answer this query, the DASH mission takes Hera’s goals as its own to propose an innovative distributed framework that can match current asteroid exploration missions at significantly lower cost. As a result, this report deals with the design of the system and subsystem elements of the DASH mission, short for Distributed Asteroid Surveying Herd. Consequently, it is fitting to first analyse the current state of the market and then proceed with the mission-dependent elements.