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Dependable Neural Networks for Identifying Anomalous Behavior in Real-Time Operations, Year 1
Completed
TRL 3 (started at 2, targeting 3)
Description
Leverage the large historical MSL data set to develop an artificial intelligence system that will automatically identify and report on issues with data transfer, archive, and manipulation throughout the Ground Data System (GDS) process in real time.
Benefits
Tracking data flow issues is highly manual and time-consuming. Data accountability, ensuring that data sent to Earth by a spacecraft is received and processed successfully, is challenging on many missions. The goal for this project is to develop and demonstrate methods for ensuring data accountability in the Ground Data System (GDS) through the application of machine learning techniques.
Details
| Technology area | Air Traffic Management and Range Tracking Systems > Traffic Management Concepts |
| Program | Center Innovation Fund: JPL CIF (JPL CIF) |
| Lead organization | Jet Propulsion Laboratory, Pasadena, CA |
| Start date | 2018-10-01 |
| End date | 2019-09-30 |
Project contacts
Listed on TechPort itself — the most direct way to ask about this specific project.
How to get involved
This is early/mid-stage (TRL 3) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.
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