Long-term Sustainable Net-centric Framework for Space Surveillance Networks (LOSSLESS)

ABSTRACT: The United States Space Surveillance Network (SSN) is a critical part of the US Strategic Command's (USSTRATCOM) missions and involves detecting, tracking, cataloging and identifying the numerous artificial objects of various natures orbiting the Earth. The modernization requirement of the SSN faces three key challenges: 1) how to seamlessly upgrade the SSN with modern COTS hardware without software porting; 2) how to improve the accuracy of target tracking by collaborating geographically spread sensors; 3) how to dynamically schedule tracking tasks to respond to unexpected space events. Intelligent Automation, Inc. propose a Long-term Sustainable Net-centric Framework for Space Surveillance Networks (LOSSLESS). LOSSLESS can run both legacy and new software on top of modern COTS hardware by leveraging the state-of-the-art Virtual Machine technologies. With the proposed distributed net-centric data fusion algorithm, LOSSLESS has a potential to improve the tracking accuracy of the current SSN. The dynamic tasking algorithm can be applied to maintain the SSN"s day-to-day tracking missions and dynamically re-task the sensor assets to manage emergency space events. In Phase I we developed a preliminary system to show its feasibility. We will further develop a fully functional prototype system to demonstrate its effectiveness, efficiency and practicality in Phase II. BENEFIT: The proposed solution has great potential in various military applications. The legacy systems (both hardware and software) of the United States Space Surveillance Network (SSN) require major capital investment to re-host and rewrite the software for the upgrades. Similar cases are happening for the updates of many military weapon control hardware and software systems (e.g., F-16 general avionics computer, AC-130H gunship mission computer SKC-3007A, MH-60K mission computer AP-102A, etc.). Potential applications of our LOSSLESS framework include SSN and military weapon system. The market size of the upgrading military legacy systems is expected to grow rapidly as more and more legacy computer system cannot meet the update to date mission requirements. The dynamic tasking algorithms may add value to warfighter"s dynamic resource management and mission analysis within various C4ISR systems. Beyond DOD, we also plan to transition the technologies to commercial applications, such as commercial avionics radars, weather monitoring systems, and air traffic management. The size of the market is quite large and will keep growing rapidly with the commercial demand of upgrading the system with modern COTS hardware and the needs of dynamic resource tasking of resources in modern networked environments.