CubeSats are a relatively new category of space systems that is becoming one of the key players for scientific and technological missions in Low Earth Orbit. Lately, interesting concepts for interplanetary nanosatellite missions are also appearing. These missions are affected by several technical limitations, spanning different domains of the mission and system design. Among these, limitations in the data rate and lack of proper ground support are definitely important. This paper focus the attention on event detection capabilities, with the intent of enabling autonomous operations for a nanosatellite interplanetary mission. The paper presents an artificial intelligence algorithm based on the neural network technology, and applies it to a future mission used as case study. The algorithm robustness is also verified.
Neural networks for event detection: an interplanetary cubesat asteroid mission case study / Feruglio, Lorenzo; Corpino, Sabrina; Calvi, Daniele. - STAMPA. - (2016), pp. 1-5. (Intervento presentato al convegno AIAA SPACE & ASTRONAUTICS FORUM & EXPOSITION 2016 tenutosi a Long Beach, California nel 13-16/09/2016) [10.2514/6.2016-5615].
Neural networks for event detection: an interplanetary cubesat asteroid mission case study
FERUGLIO, LORENZO;CORPINO, Sabrina;CALVI, DANIELE
2016
Abstract
CubeSats are a relatively new category of space systems that is becoming one of the key players for scientific and technological missions in Low Earth Orbit. Lately, interesting concepts for interplanetary nanosatellite missions are also appearing. These missions are affected by several technical limitations, spanning different domains of the mission and system design. Among these, limitations in the data rate and lack of proper ground support are definitely important. This paper focus the attention on event detection capabilities, with the intent of enabling autonomous operations for a nanosatellite interplanetary mission. The paper presents an artificial intelligence algorithm based on the neural network technology, and applies it to a future mission used as case study. The algorithm robustness is also verified.Pubblicazioni consigliate
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https://hdl.handle.net/11583/2657678
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