The United States Space Surveillance Network (SSN) is a critical part of the United States Strategic Command's (USSTRATCOM) mission. Using dish radars, phased array radars and optical systems, SSN detects, tracks, catalogues and identifies artificial objects orbiting the Earth, including active and inactive satellites, spent rocket bodies, or fragmentation debris. Modernizing the aging systems of the SSN requires addressing the challenge of seamlessly upgrading SSN with modern COTS hardware, without software porting. Further, the accuracy of tracking targets has to be improved by collaborating geographically spread sensors. In addition, tasks have to be dynamically scheduled to respond to unexpected space events. To address these issues, IAI has been awarded a contract entitled, “Long-term Sustainable Net-centric Framework for Space Surveillance Networks (LOSSLESS).” LOSSLESS can run both legacy and new software on top of modern commercial-off-the-shelf (COTS) hardware by leveraging state-of-the-art Virtual Machine (VM) technologies. A distributed net-centric data fusion algorithm will be used to significantly improve the tracking accuracy of current SSN. LOSSLESS can also respond to unexpected dynamic space events by using an innovative dynamic tasking algorithm to maintain SSN’s day-to-day tracking missions, and dynamically re-task sensor assets to manage emergency space events. The proposed LOSSLESS framework can meet the future requirement of space target tracking for the SSN. The feasibility of LOSSLESS has been demonstrated using a preliminary system, and a fully functional prototype will be developed to demonstrate its effectiveness, efficiency and practicality. Possible commercial applications of LOSSLESS include upgrading legacy systems, especially for radar or electro-optical (EO) sensor sites. Dynamic tasking techniques can be applied to any dispersed, networked systems, and the data pre-processing techniques to any radar or EO system transforming data to relevant information.