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Satellite Anomaly Injection & Detection (SAID) Testbed
Sponsored by The Aerospace Corporation
Martha Caldera, Diana Degiacomo, Gabriel Kutasi, Jae Lee, Michael Morris, Gustavo Torres, Tomas Velarde, Dearo Yam, Rafael Zaragoza
Advisor: Zilong Ye

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 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, we have developed software components to integrate with and utilize existing industry open-source software components to perform the tasks outlined below to:

  1. Generate satellite simulation data
  2. Inject anomalous scenarios into the flight system
  3. Apply techniques for detecting the anomalies onboard and on the ground

Outcomes from the project:

  1. Software source to developed anomaly injection and detection capabilities
  2. Detailed documentation on design, implementation, tests, and results from each of the anomaly scenarios
  3. User manual to set up, configure, and run the OSK with the anomaly injection and detection capabilities
  4. Monthly review meetings with Aerospace liaisons and final outbrief to Aerospace engineers
     

 

Team Meetings with Advisor: Friday 2pm (with liaisons every 3 weeks)

Zoom Linkhttps://calstatela.zoom.us/j/88631222180

 

Student Led Meetings: Monday 6pm on Discord

 


Team / Contact Info
Roles 
(details of Project Organization in Resources)
Name Email
 Project Leads 

 - Dearo Yam

 - Diana Degiacomo 

dearoyam.edu@gmail.com

degiacomodiana@gmail.com

Documentation Leads

 Diana Degiacomo

 Gabe Kutasi

degiacomodiana@gmail.com

fisherman_nab@yahoo.com

Documentation Team

 Martha Caldera       

 Rafael Zaragoza

mcaldera2@yahoo.com

raf.zaragoza@gmail.com

Code Development Leads

 Dearo Yam

 Gustavo Torres

dearoyam.edu@gmail.com

gtorres9912@gmail.com

Code Development Team

 Michael Morris

 Tomas Velarde

 Jae Lee

michaeldmorris009@gmail.com

tomasplaceholder@gmail.com

Leejoe0368@gmail.com

Resources
2022
Aerospace Project Proposal
Preliminary Design Review
SAID December 3, 2021 - Presentation
SAID: SRS
Poster
Video Link
Spring Final Slides
Final Report
DDOS Detection Code
DDOS Injection Code
Machine Learning Code
Single Bit Error Github
DoS Github