Space Force Satellite Scheduling

Stottler Henke has also worked on satellite control network scheduling software that uses the Aurora framework.

US Space Operations

• MIDAS – Managed Intelligent Deconfliction and Scheduling for the Satellite Control Network
  • The Satellite Control Network (SCN) handles hundreds of satellite communication requests daily, many of which require conflict resolution. MIDAS (Managed Intelligent Deconfliction and Scheduling) automates much of this work, reducing the burden on expert schedulers. It uses a two-stage process: first adjusting requests within constraints, then applying customizable business rules when flexibility is needed. With a user-friendly interface, low hardware demands, and compatibility with legacy systems, MIDAS supports faster deconfliction, planning, and training. Read more.
• SSN Scheduling – Improved Space Surveillance Network (SSN) Scheduling
  • The Space Surveillance Network (SSN) tracks nearly 20,000 objects in orbit, most of which are inactive debris. Using existing radar and optical sensors, a better observation schedule can improve catalog accuracy, track more objects, and respond faster to real-time changes. Our algorithm generates optimized schedules for each sensor, determining which objects to observe and when, so that measurements are more complementary and precise. This approach increases accuracy across the entire catalog, reduces lost objects, and makes more efficient use of current resources. Read more.
• MALINA – Automated Performance Assessment for Training Satellite Planners Based on Learned Metrics
  • Satellite Operations Centers (SOCs) must balance numerous mission-specific constraints when scheduling satellite contacts, making planner training and performance assessment essential. MALINA (Modular Annotated Learning for Instructional Authoring) simplifies this by combining machine learning with instructor-provided annotations to create assessment measures from real-world data and expert input. Initially applied to SOC training, MALINA generated assessments from historical scheduling tasks involving multiple satellites and constraints, enabling both feedback and solution comparisons. Beyond training, MALINA’s ability to learn decision-making rules offers value for operational support, suggesting solutions to complex scheduling challenges. Read more.
• TRACER – Intelligent Terrestrial EMI Emitter Locator for SCN Ground Stations based on AI Techniques
  • The Satellite Control Network (SCN) relies on large dish antennas to communicate with U.S. satellites, making protection from electromagnetic incursions (EMIs) essential. EMIs occur when outside signals overlap with satellite communication frequencies, potentially disrupting command and control. To address this, Stottler Henke developed TRACER, a system that integrates data management, tasking, analysis, and visualization to streamline EMI detection and localization. TRACER enhances space situational awareness, reduces manpower needs, accelerates response times, and supports defense against threats such as jamming. Read more.
• RAPTOR – RFI Detection and Prediction Tool
  • RAPTOR is an enhancement of Stottler Henke’s Aurora scheduling system, designed to improve Satellite Control Network (SCN) operations by automating scheduling, deconfliction, and information sharing across Satellite Operations Centers. Using AI techniques, RAPTOR produces higher-quality schedules, resolves conflicts automatically or semi-automatically, and supports far-future, contingency, and real-time planning. Its intuitive interfaces allow schedulers, operators, and commanders to manage both satellite constellations and individual satellites efficiently. Beyond the SCN, RAPTOR’s capabilities can also benefit NASA’s space communications networks, including the DSN, SN, and GN. Read more.
• PASAP – Optimization of Communication Networks with Geodesic Dome Phased Array Antennas using Artificial Intelligence Techniques

  • PASAP (Phased Array Smart Allocation and Planning) uses artificial intelligence to optimize communications for Geodesic Dome Phased Array Antennas (GDPAA). It combines a smart beam allocation algorithm, which prevents overloads when assigning transmit/receive modules, with a beam path planning algorithm adapted from U.S. Army aviation applications. Together, these algorithms create efficient beam paths across the dome surface while respecting communication constraints. Though designed for GDPAA, PASAP’s abstract framework can be applied to other phased array systems, reducing manpower needs and expanding overall satellite communication capacity. Read more.

• MARS – MIDAS Automated Resource Scheduler
  • MARS is a globally distributed, real-time system for creating, editing, and executing satellite communication schedules, built to reduce scheduler workload by over 90% through automated deconfliction. It supports real-time conflict checking, rapid schedule changes, and distributed collaboration with automatic alerts and notifications. The system generates 24-hour schedules for hundreds of satellites and thousands of tasks within minutes, handling both hard and soft constraints across multiple constellations and SOCs. With fault tolerance, backup capabilities, and tools for quality assessment, RFI investigation, and user communication, MARS provides a robust, flexible platform for worldwide satellite scheduling. Read more.