Early assessment of drone fleet defence in depth capabilities for mission success

Nikolaos Papakonstantinou, Ahmed Z. Bashir, Bryan O'Halloran, Douglas L.Van Bossuyt

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

    Abstract

    Advancements in the domain of artificial intelligence, safety management, and on-board fault tolerance have led to autonomous devices to be considered as a key element for future remote defence and peaceful missions. Drones-Also known as autonomous or unmanned vehicles-with different capabilities and features-can be organized in a fleet and the fleet can be organized in a way that will increase the survivability of the drones and improve mission success. This can be accomplished by balancing system effectiveness design parameters such as endurance, communications, sensor fusion, domain awareness, area coverage rates and human operator interaction against mission costs.

    Original languageEnglish
    Title of host publicationRAMS 2019 - 2019 Annual Reliability and Maintainability Symposium, Proceedings
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Number of pages7
    ISBN (Electronic)978-1-5386-6554-1
    DOIs
    Publication statusPublished - 2019
    MoE publication typeA4 Article in a conference publication
    Event2019 Annual Reliability and Maintainability Symposium, RAMS 2019 - Orlando, United States
    Duration: 28 Jan 201931 Jan 2019

    Conference

    Conference2019 Annual Reliability and Maintainability Symposium, RAMS 2019
    CountryUnited States
    CityOrlando
    Period28/01/1931/01/19

    Keywords

    • Defence in Depth
    • Drone fleets
    • Mine Counter Measures
    • Model driven engineering

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  • Cite this

    Papakonstantinou, N., Bashir, A. Z., O'Halloran, B., & Bossuyt, D. L. V. (2019). Early assessment of drone fleet defence in depth capabilities for mission success. In RAMS 2019 - 2019 Annual Reliability and Maintainability Symposium, Proceedings IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/RAMS.2019.8769017