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Missile Defense Applications

Stottler Henke has developed a range of applications for missile defense problems related to Golden Dome, with concentrations in three main technology areas:

  •       Automatic, Intelligent Tactical Space Application Scheduling Systems
  •       Automatic, Intelligent Space Domain Awareness (SDA) Systems
  •       Spacecraft Anomaly Detection and Diagnosis Systems

Specifically we have implemented automatic, intelligent scheduling systems for a variety of space and missile defense applications. These execute extremely quickly to generate high quality scheduling, resource assignment, and tasking decisions, while being highly scalable and applicable to a wide range of arbitrary problems. Examples include scheduling space/ground-based sensors and effectors, and network data communications. Examples of Stottler Henke’s relevant innovative projects are listed below.

OPIR Sensor Scheduling

Automatic, intelligent application for scheduling Overhead Persistent Infrared (OPIR) sensors to achieve optimized utilization of existing sensors in the Joint OPIR Center (JOPC).

Satellite Scheduling

For the Satellite Control Network (SCN), Stottler Henke developed the MIDAS Automated Resource Scheduler (MARS) system, a program of record.  MARS can automatically process and deconflict a schedule in 5 minutes – a dramatic improvement on previous manual scheduling procedures requiring 18-24 person-hours.  

Missile Defense Scheduling

MD-Sched is an artificial intelligence-based scheduling system for the Missile Defense Agency (MDA), to improve real time simultaneous assignment and scheduling for sensors and ground / sea / air / space based interceptors.

SDA Sensor Scheduling

High-speed / high-quality sensor schedulers for the space surveillance network (SSN) and commercial space domain awareness (SDA) sensors.  Stottler Henke’s schedulers are capable of scheduling 50,000 sensing tasks on 100s of sensors while considering 1.5 million visibilities in under 4 seconds!

Space Network Data Scheduling

DREAMS (Delay/disruption tolerant REinforcement learning and Aurora Based coMmunication System) optimizes data scheduling, routing, and satellite contacts to maximize throughput in a space communication network.  The DREAMS system is integrated with NASA’s LunaNet, including flight testing.

Multi-Beam Antenna Scheduling

BAMBA (Bottleneck Avoidance for Multi Beam Antennas) is an intelligent distributed scheduling system for an important Navy Tactical Network to efficiently and quickly route tactical data through Naval battle groups utilizing multi-beam antennas.

SHERLOC SDA Inference

In order to help space domain awareness (SDA) decision-makers avoid surprise from objects in space, Stottler Henke’s SHERLOC tools are designed to use automated reasoning from multi-source evidence to help interpret the current situation in real-time based on data and inference from past cases.

IR/Radar Data Fusion

Automated processing of infrared and radar images for enhanced correlation, classification, and lethality discrimination for ballistic missile defense targets.  Stottler Henke developed a sensor data fusion system applying artificial intelligence techniques to extract features from raw sensor data and reason about those features and their associations with hypothetical objects.

Spacecraft Subsystem Fault Diagnosis

Our MAESTRO architecture for fault detection and diagnosis has been applied to different spacecraft subsystem management problems and for detecting radiation-induced faults on computational and electrical power system (EPS) hardware.  MAESTRO has been applied and tested in orbit on the International Space Station (ISS), a Montana State University cubesat EPS, and numerous other applications.

PWSA Satellite Fault Diagnosis

Stottler Henke is adapting the MAESTRO fault diagnosis system for 3 different Proliferated Warfare Space Architecture (PWSA) satellites.  These implementations integrate a set of methodologies based on Model Based Reasoning (MBR), Self-Organizing Maps (SOMs), Case-Based Reasoning (CBR), and a modular system called TRIAD which focuses on intelligent aggregation of time-series anomaly detection methods.