SAID Testbed

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Capstone Senior Design 2020-2021 Project
COMPUTER SCIENCE PROGRAM

SATELLITE ANOMALY INJECTION & DETECTION TESTBED
CLIENT: THE AEROSPACE CORPORATION
LIAISON: RICK JOHNSON, DENNY LY, KARINA MARTINEZ, PABLO SETTECASE
Students: Matthew Gilligan, Alex Huang, Alexander Lopez, Jerome Pineda, Vivian Sau, Samantha Simpson, Aaron Tong, Nicholas Torres, Joshua Tran
Advisor: Russ Abbott

Satellites perform a host of vital functions including communications, weather prediction, geolocation, defense, and many others. In these complicated systems, it is extremely important that accurate data flows freely between the ground and the satellite via uplinks and downlinks. When strange behaviors or anomalies occur, it is vital that the errors be identified and corrected before a disaster occurs. Sometimes these anomalies are the result of errors in the hardware or software, issues introduced by the environment, or an attack by a hacker. Effective anomaly detection techniques can help identify problems on the vehicle before they happen, which can help improve mission success.

The operation of satellites in long-term operation is affected by many uncertain factors. Anomaly detection based on telemetry data is a critical satellite health monitoring task that is important for identifying unusual or unexpected events. The use of simulation tools allows users to configure and deploy platforms to be used in real-time environments as well as simulate any anomalies that can take place. Machine learning can be used to detect these anomalies by comparing actual observed values with the predicted intervals of telemetry data. Simulation tools can be utilized by students to develop a way to solve these complex problems using applications already being used in the industry.

For this project, students will develop software components to integrate with and utilize existing industry open-source software components to perform the tasks outlined below to:
• Generate satellite simulation data
• Inject anomalous scenarios into the flight system
• Apply techniques for detecting the anomalies on board and on the ground

Expected outcomes from the project:
• Software source to developed anomaly injection and detection capabilities
• Detailed documentation on design, implementation, tests, and results from each of the anomaly scenarios
• User manual to set up, configure, and run the OSK with the anomaly injection and detection capabilities

https://www.calstatela.edu/ecst/seniordesign/expo-2021
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