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Email: captain@ubcorbit.com

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Projects

UBC Orbit develops new technologies and designs constantly, but our efforts are currently concentrated on three main projects. Keep checking this page and follow us on social media for updates as they occur!

Current Projects

ORCASAT

ORCASat (Optical and Radio CAlibration CubeSat) is a multi-payload, collaborative effort between teams at the University of British Columbia, Simon Fraser University, the University of Victoria, and Instituto Superior Técnico in Lisbon, Portugal.

ORCASat will be used to calibrate ground based star photometers and observatories for atmospheric and instrumental attenuation. Additionally, its radio source will be used to calibrate the CHIME (Canadian Hydrogen Intensity Mapping Experiment) radio observatory. The CubeSat also provides a store and forward repeater, as service to the amateur radio community, and a slow scan TV beacon for STEM outreach.

 
 

ASPECTU

Aspectu (Latin for sight) provides a satellite imagery service 

which captures images of Earth on an on-demand basis, with a spectral resolution of 53 meters, and downlinks the images to amateur radio operators worldwide. 

Some of its other functions include:

  • Quantifying the computational and power requirements of onboard image processing.

  • Evaluating the performance of various computer vision and machine learning algorithms for the purposes of minimizing communication bandwidth.

 

TRILLIUM

Trillium is a low cost alternative to radiation hardened micro-controllers (MCUs) for space computation through the use of triple redundant MCUs.

Trillium is used as the Command and Data Handling subsystem in Aspectu, and provides automatic on-board satellite operation, along with command-driven operation when requested by ground. Trillium must be a self-sustaining system, able to detect and handle any on-board errors.

Past Projects

THUNDERBIRD

The Thunderbird Satellite's primary payload consists of two cameras intended for high-resolution imaging of Earth. The photos are evaluated for potential forest fire risks using on-board machine learning modules, trained on the ground, with capability to update these modules over the course of the mission.

The use of two cameras enables multiple filters to be applied to the image sensor, allowing for infrared and visible light imaging.