Using Amazon SageMaker AI Random Cut Forest for NASA’s Blue Origin spacecraft sensor data

This article demonstrates how Amazon SageMaker's Random Cut Forest (RCF) algorithm can be used to detect anomalies in spacecraft sensor data from NASA's Blue Origin lunar mission.

• Applies unsupervised machine learning to analyze spacecraft position, velocity, and orientation data
• Uses RCF algorithm to identify unusual patterns in 10-dimensional spacecraft dynamics data
• Processes telemetry data from spacecraft deorbit, descent, and landing phases
• Provides visualization of detected anomalies in position, velocity, and quaternion measurements
• Enables enhanced mission analysis and potential early detection of spacecraft system issues

The solution demonstrates how AWS cloud services and machine learning can improve spacecraft mission monitoring and analysis, offering potential applications across space exploration and satellite operations.