SHERLOC-Multi: Multi-Object Space Threat Assessment Using Data Fusion and Other AI Techniques

Space battle management and command and control depend heavily on maintaining space domain awareness (SDA) to protect and defend our space assets.  Hostile activities conducted by adversaries may involve more than a single event and a single object, when the underlying strategic purpose is concerned with effects on the operating capabilities of multiple satellites.  Stottler Henke proposes to develop a multi-object threat reasoning tool called SHERLOC-Multi, that applies data fusion and case-based reasoning (CBR) to the challenge of characterizing coordinated multi-object threats.  This addresses an operator need for tools to process data and facilitate faster decision-making, not only to detect each emerging potentially hostile event, but also gain awareness of the possible relationships between multiple events, and possible intended effects.  SHERLOC-Multi builds on past research and testing in an operational setting, with automated methods using level-two data fusion to process combined information from sensor data with multiple phenomenologies, outputs from analytic tools, and human characterizations.  Planned Phase I research includes a limited proof-of-concept prototype focusing on examples in geosynchronous orbit, for example where multiple adversary spacecraft may coordinate on an inspection, jamming, or kinetic threat.  This forms the foundation for an expanded operational prototype in Phase II.