Stouch, D., Jenkins, M., McCaffrey, J., and Catto, G.
Air Force Research Laboratory Space Situational Awareness Conference, Kihei, HI (September 2018)
To effectively conduct battle management command and control (BMC2) in space, operators need to understand the space environment well beyond what is required for traffic management that predicts potential conjunctions. Operators need to know what specific threats might affect their high value asset (HVA) spacecraft in the future, the potential impacts of these threats, how they might avoid negative impacts, and what the likelihood of success for each candidate countermeasure or avoidance maneuver.
As part of DARPA’s Hallmark program, we have developed prototype capabilities to visualize the space threat environment in 3D, understand the likelihood and potential effects of numerous proximity attacks, and model and assess some basic courses of action to avoid or mitigate impacts on overall mission performance. Effective space operations rely on distributed understanding of a dynamic and complex information environment. Given the massive array of relevant factors that must be tracked and considered throughout space operations planning and execution, and the spatiotemporal nature of that information, available data and relevant abstractions must be placed in context of course of analysis (COA) generation, and operators must be directly immersed in that context to develop a natural intuition for potential threats and effective responses.
We use augmented and mixed reality (AR/MR) to provide the support to augment and enhance traditional information displays with immersive, context-driven, multi-dimensional, collaborative, and user-configurable information visualizations that will provide revolutionary Space Situational Awareness (SSA) and decision-making support. One aspect of this supports effective, in-situ multi-level security (MLS) access with simultaneous real-time interaction at lower security access levels. Multiple operators can view and interact with a common operating picture (COP) while separately using AR to independently visualize intel reports, asset status, threats, and capabilities that are restricted to their specific operational role, technical specialty, or security clearance. Another aspect of our solution encodes threat indications and warnings (I&W) as probabilistic relational models (PRM) to help shape space defense plans by defining appropriate threshold levels at which to escalate notifications of potential adversarial activity. The analytic tool produces a probability distribution over possible outcomes to identify the ‘most significant’ threats according to various categories such as kinetic, electromagnetic, and cyber. These capabilities are related to threat object origins, potential adversary capabilities, and proximity of closest approach to quantify the threats. Our tool also supports analysis of low level threats against satellite constellations (e.g., early warning; communications; intelligence, surveillance, and reconnaissance (ISR)) and groups of satellites that are actively supporting specific operational missions (e.g., treaty compliance, COCOM directed missions, border disputes).
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