Multi-Object Behavior Modeling of Space Systems

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Space Technology The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Investigate and develop tactical-speed analytical capabilities for space multi-threat, multi-object characterization and assessment. DESCRIPTION: A fundamental component of ensuring freedom of operation in space is space battle management and command and control (SBMC2), with a core tenet therein being the ability to conduct real-time analysis of the risk to mission. This is necessary to allow action to be taken accurately, knowledgeably, and on appropriate timelines. This concept requires having knowledge. Collected data can be used to provide information to form hypotheses, but these hypotheses must have meaningful and critical context for BMC2 decision-making. Thus, accurate characterization on mission-relevant timelines must determine not only the behavior of a space-based threat but also the potential impact to the mission. In the increasingly complex space domain, analyses should look beyond single-object, single-event occurrences. Traditional approaches to modeling multi-object and/or campaign-level activities is time consuming and labor intensive, carries high levels of bias risk, and does not easily accommodate iteration for comparative and/or what-if analysis. Improved and new capabilities must support more complex analyses on tactically-relevant timelines and enable a robust risk assessment to include likelihood and consequence of events. The technology to be developed should consider multiple behavioral and physics inputs, perform threat and impact analysis beyond single object and event actions, and present assessment results in mission-time for critical decision making. PHASE I: Awardees for Phase I will conduct a feasibility study on candidate technologies and develop an architecture that addresses the needs described in the topic description. This feasibility study should include assessments of computational methods for autonomously processing large amounts of space data. The feasibility study should be supported with preliminary analysis and results to demonstrate the viability of the approach. PHASE II: Phase II efforts will design, develop, and implement a prototype based on the architecture developed in Phase I, focused on multi-object engagements within a single orbital regime with flexibility and scalability to expand into other regimes. The Phase II prototype shall generate situational awareness and risk to mission calculations using synthetic, representative, data. PHASE III DUAL USE APPLICATIONS: Phase III efforts would involve enhanced performance capabilities of the prototype architecture implementation. They will demonstrate autonomous assessment capabilities as part of military exercises and other representative operational environments. Working with transition partners, they will identify and evaluate opportunities for implementation/integration in DoD and/or civilian applications requiring timely data for situational awareness. REFERENCES: 1. Jah, M., Space Object Behavior Quantification and Assessment for Space Security. In: Handbook of Space Security, Springer, 2020, doi:10.1007/978-3-030-22786-9_103-1 2. Furfaro, R., Linares, R.; Gaylor, D., Jah, M., Walls, R., Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks, Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, Maui, HI, 2016. 3. DeMars, K., Frueh, C., Jah, M, Erwin, R., Multiple-Object Space Surveillance Tracking Using Finite-Set Statistics. Journal of Guidance, Control, and Dynamics, 2020, doi:10.2514/1.G000987. KEYWORDS: Space domain awareness; machine learning; AI, space command control