Quaternion-based attitude estimation of an aircraft model using computer vision

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dc.contributor.authorKasula, Pavithra
dc.contributor.authorWhidborne, James F.
dc.contributor.authorRana, Zeeshan A.
dc.date.accessioned2024-06-20T10:52:20Z
dc.date.available2024-06-20T10:52:20Z
dc.date.issued2024-06-12
dc.description.abstractInvestigating aircraft flight dynamics often requires dynamic wind tunnel testing. This paper proposes a non-contact, off-board instrumentation method using vision-based techniques. The method utilises a sequential process of Harris corner detection, Kanade–Lucas–Tomasi tracking, and quaternions to identify the Euler angles from a pair of cameras, one with a side view and the other with a top view. The method validation involves simulating a 3D CAD model for rotational motion with a single degree-of-freedom. The numerical analysis quantifies the results, while the proposed approach is analysed analytically. This approach results in a 45.41% enhancement in accuracy over an earlier direction cosine matrix method. Specifically, the quaternion-based method achieves root mean square errors of 0.0101 rad/s, 0.0361 rad/s, and 0.0036 rad/s for the dynamic measurements of roll rate, pitch rate, and yaw rate, respectively. Notably, the method exhibits a 98.08% accuracy for the pitch rate. These results highlight the performance of quaternion-based attitude estimation in dynamic wind tunnel testing. Furthermore, an extended Kalman filter is applied to integrate the generated on-board instrumentation data (inertial measurement unit, potentiometer gimbal) and the results of the proposed vision-based method. The extended Kalman filter state estimation achieves root mean square errors of 0.0090 rad/s, 0.0262 rad/s, and 0.0034 rad/s for the dynamic measurements of roll rate, pitch rate, and yaw rate, respectively. This method exhibits an improved accuracy of 98.61% for the estimation of pitch rate, indicating its higher efficiency over the standalone implementation of the direction cosine method for dynamic wind tunnel testing.en_UK
dc.identifier.citationKasula P, Whidborne JF, Rana ZA. (2024) Quaternion-based attitude estimation of an aircraft model using computer vision. Sensors, Volume 24, Issue 12, June 2024, Article number 3795en_UK
dc.identifier.eissn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s24123795
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22532
dc.language.isoen_UKen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectcomputer visionen_UK
dc.subjectextended Kalman filteren_UK
dc.subjectinertial measurement unit; quaternionsen_UK
dc.subjectEuler anglesen_UK
dc.subjectcomputer-aided designen_UK
dc.subjectdynamic wind tunnel testingen_UK
dc.subjectflight dynamicsen_UK
dc.titleQuaternion-based attitude estimation of an aircraft model using computer visionen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2024-06-06

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