@inproceedings{de4c61bd7ac740d4a7310066ede494bf,
title = "On-Board Anomaly Detection on a Flight-Ready System",
abstract = "Within the scope of an ESA funded activity, Airbus Defence and Space GmbH completed a research and development study in order to provide a novel dataset to ESA and develop a flight-ready system for on-board anomaly detection. This work includes the extraction of satellite telemetry data, the identification of anomalies, the development of machine learning models and the flight-ready system and finally the deployment of the machine learning algorithms via hardware acceleration. We present the benchmarking results of three accelerated ML algorithms from within the final flight-ready system.",
keywords = "FPGA, Machine learning, PUS, anomaly detection, co-processor, on-board AI",
author = "Andreas Koch and Alisa Krstova and Florian Hegwein and {De Lera}, {Mario Castro} and Filippo Ales and Michael Petry and Rashid Ali and Maen Mallah and Laurent Hili and Max Ghiglione and Martin Werner",
note = "Publisher Copyright: {\textcopyright} 2023 ESA.; 2023 European Data Handling and Data Processing Conference for Space, EDHPC 2023 ; Conference date: 02-10-2023 Through 06-10-2023",
year = "2023",
doi = "10.23919/EDHPC59100.2023.10395967",
language = "English",
series = "Proceedings of the 2023 European Data Handling and Data Processing Conference for Space, EDHPC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Maris Tali and Max Ghiglione",
booktitle = "Proceedings of the 2023 European Data Handling and Data Processing Conference for Space, EDHPC 2023",
}