The ARMAS-SOM framework fuses collaborative all-source sensor information in a resilient manner with fault detection, exclusion, and integrity solutions recognizable to a GNSS user. This framework uses a multifilter residual monitoring approach for fault detection and exclusion, which is augmented with an additional “observability” extended Kalman filter sublayer for resilience. We monitor the a posteriori state covariances in this sublayer to provide intrinsic awareness when navigation state observability assumptions required for integrity are in danger. The framework leverages this to selectively augment with offboard information and preserve resilience. By maintaining split parallel collaborative and proprioceptive instances and employing the “stingy collaboration” technique, we are able to maximize efficient use of network resources, limit the propagation of unknown corruption to a single donor, and maintain consistent collaborative navigation without fear of double-counting in a scalable processing footprint. Last, we preserve the ability to return to autonomy and are able to use the same intrinsic awareness to notify the user when it is safe to do so.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Framework for Collaborative All-Source Navigation With Fault Detection and Exclusion


    Beteiligte:


    Erscheinungsdatum :

    01.10.2022


    Format / Umfang :

    2124343 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Fault Detection and Exclusion in Multisensor Navigation Systems

    Bernath, G. / National Aeronautics and Space Administration| Federal Aviation Administration | British Library Conference Proceedings | 1995



    Collaborative Fault Detection in Resilient Navigation

    Wang, Rong / Xiong, Zhi / Liu, Jianye | Springer Verlag | 2023