Improves the processing, verification, and validation for the Space Launch System. Highlights include efficient scheduling and fewer requirements for human planners.
|Aurora AMP (Automated Manifest Planner) for Space Shuttle Scheduling
Performs long and short-term schedules for ground-based activities and before and after space missions.
|MIDAS (Managed Intelligent Deconfliction and Scheduling) for the Air Force Satellite Control Network
Provides a user-friendly interface modeled on legacy Electronic Schedule Dissemination (ESD) systems. It runs on inexpensive consumer hardware and communicates with legacy systems via a well-defined plain-text file format.
Scheduling algorithm that takes as input the space catalog and the associated covariance matrices and produces a globally optimized schedule for each sensor site as to what objects to observe and when. Is able to schedule more observations with the same sensor resources and have those observations be more complementary.
|IFAP for ISS Scheduling
Used to assign flight activities, enabling the planners to quantify the risks of each plan. Planners control the scheduling process by choosing from a list of intelligent heuristics.
|PASAP (Phased Array Smart Allocation and Planning)
Automated deconfliction and path planning technology developed to enable phased array antennas to support multiple simultaneous satellite contacts without overlapping active areas.
Provides an integrated set of data management, task management, analysis, and data visualization capabilities. These capabilities improve space situational awareness, reduce manpower requirements, dramatically shorten EMI response time, enable the system to evolve without programmer involvement, and support adversarial scenarios such as jamming.
|RAPTOR (RFI Detection and Prediction Tool)
Provides better-quality schedules, faster scheduling, and the ability to handle larger, more complex sets of requests. RAPTOR negotiates resolution of conflicts in an automated or semi-automated manner and performs far-future and automatic abnormal real-time scheduling signal detection and prediction.
Produces real-time threat assessments for space domain awareness (SDA) using probabilistic reasoning from multi-source information ranging from sensor data and pattern-of-life analytics to open source intelligence.
Applies machine learning to construct performance assessment knowledge for training satellite planners.