On-Board Anomaly Detection on a Flight-Ready System

Andreas Koch, Alisa Krstova, Florian Hegwein, Mario Castro De Lera, Filippo Ales, Michael Petry, Rashid Ali, Maen Mallah, Laurent Hili, Max Ghiglione, Martin Werner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the 2023 European Data Handling and Data Processing Conference for Space, EDHPC 2023
EditorsMaris Tali, Max Ghiglione
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9789090379241
DOIs
StatePublished - 2023
Event2023 European Data Handling and Data Processing Conference for Space, EDHPC 2023 - Juan-Les-Pins, France
Duration: 2 Oct 20236 Oct 2023

Publication series

NameProceedings of the 2023 European Data Handling and Data Processing Conference for Space, EDHPC 2023

Conference

Conference2023 European Data Handling and Data Processing Conference for Space, EDHPC 2023
Country/TerritoryFrance
CityJuan-Les-Pins
Period2/10/236/10/23

Keywords

  • FPGA
  • Machine learning
  • PUS
  • anomaly detection
  • co-processor
  • on-board AI

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