The primary objective for the Calypso Cubesat is to collect atmospheric data to allow the use of deep learning on optical communication systems. Free space optical communication techniques have been the subject of numerous investigations in recent years, with multiple missions expected to fly in the near future. Existing methods require high pointing accuracies, dramatically driving up overall system cost. Recent developments in LED-based visible light communication (VLC) and past in-orbit experiments have convinced us that the technology has reached a critical level of maturity. On these premises designed a new optical communication system utilizing a VLC downlink and a high throughput, omnidirectional photovoltaic cell receiver system.
By performing error-correction via deep learning methods and by utilizing phase-delay interference, the system is able to deliver data rates that match those of traditional laser-based solutions. Calypso will allow us to mature the technology and to provide an opportunity for the full scale development of optical communication techniques on small spacecraft as a backup telemetry beacon or as a high throughput link.
At the same time, we recognized that such a system provides an excellent platform for STEM outreach. With an apparent visible magnitude of 2.5, the transmitter complements a Raspberry Pi payload to enable real-time student interaction with the satellite and user applications to be uploaded to the payload computer.
To learn more visit the Calypso project website at http://www.calypsocubesat.com