Resilient multi-sensor UAV navigation with a hybrid federated fusion architecture

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dc.contributor.authorNegru, Sorin Andrei
dc.contributor.authorGeragersian, Patrick
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorGuo, Weisi
dc.date.accessioned2024-03-15T15:53:04Z
dc.date.available2024-03-15T15:53:04Z
dc.date.issued2024-02-02
dc.description.abstractFuture UAV (unmanned aerial vehicle) operations in urban environments demand a PNT (position, navigation, and timing) solution that is both robust and resilient. While a GNSS (global navigation satellite system) can provide an accurate position under open-sky assumptions, the complexity of urban operations leads to NLOS (non-line-of-sight) and multipath effects, which in turn impact the accuracy of the PNT data. A key research question within the research community pertains to determining the appropriate hybrid fusion architecture that can ensure the resilience and continuity of UAV operations in urban environments, minimizing significant degradations of PNT data. In this context, we present a novel federated fusion architecture that integrates data from the GNSS, the IMU (inertial measurement unit), a monocular camera, and a barometer to cope with the GNSS multipath and positioning performance degradation. Within the federated fusion architecture, local filters are implemented using EKFs (extended Kalman filters), while a master filter is used in the form of a GRU (gated recurrent unit) block. Data collection is performed by setting up a virtual environment in AirSim for the visual odometry aid and barometer data, while Spirent GSS7000 hardware is used to collect the GNSS and IMU data. The hybrid fusion architecture is compared to a classic federated architecture (formed only by EKFs) and tested under different light and weather conditions to assess its resilience, including multipath and GNSS outages. The proposed solution demonstrates improved resilience and robustness in a range of degraded conditions while maintaining a good level of positioning performance with a 95th percentile error of 0.54 m for the square scenario and 1.72 m for the survey scenario.en_UK
dc.identifier.citationNegru SA, Geragersian P, Petrunin I, Guo W. (2024) Resilient multi-sensor UAV navigation with a hybrid federated fusion architecture. Sensors, Volume 24, Issue 3, February 2024, Article number 981en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s24030981
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21012
dc.language.isoen_UKen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectUAVen_UK
dc.subjecturban air mobilityen_UK
dc.subjectcomputer visionen_UK
dc.subjectmultipathen_UK
dc.subjectresilient navigationen_UK
dc.subjecthybrid fusionen_UK
dc.subjectGRUen_UK
dc.subjectEKFen_UK
dc.titleResilient multi-sensor UAV navigation with a hybrid federated fusion architectureen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2024-01-30

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