Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80398
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dc.contributorInterdisciplinary Division of Aeronautical and Aviation Engineeringen_US
dc.creatorZhang, Gen_US
dc.creatorHsu, LTen_US
dc.date.accessioned2019-02-20T04:00:47Z-
dc.date.available2019-02-20T04:00:47Z-
dc.identifier.issn1270-9638en_US
dc.identifier.urihttp://hdl.handle.net/10397/80398-
dc.language.isoenen_US
dc.publisherElsevier Massonen_US
dc.rights© 2018 Elsevier Masson SAS. All rights reserved.en_US
dc.rights© 2018 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Zhang, G., & Hsu, L. T. (2018). Intelligent GNSS/INS integrated navigation system for a commercial UAV flight control system. Aerospace Science and Technology, 80, 368-380 is available at https://dx.doi.org/10.1016/j.ast.2018.07.026en_US
dc.subjectUAVen_US
dc.subjectGPSen_US
dc.subjectNavigationen_US
dc.subjectKalman filteren_US
dc.subjectAdaptive tuningen_US
dc.subjectMachine learningen_US
dc.titleIntelligent GNSS/INS integrated navigation system for a commercial UAV flight control systemen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage368en_US
dc.identifier.epage380en_US
dc.identifier.volume80en_US
dc.identifier.doi10.1016/j.ast.2018.07.026en_US
dcterms.abstractOwing to the increase in civil applications using quadcopters, commercial flight control systems such as Pixhawk are a popular solution to provide the sensing and control functions of an unmanned aerial vehicle (UAV). A low-cost global navigation satellite system (GNSS) receiver is crucial for the low-cost flight control system. However, the accuracy of GNSS positioning is severely degraded by the notorious multipath effect in mega-urbanized cities. The multipath effect cannot be eliminated but can be mitigated; hence, the GNSS/inertial navigation system (INS) integrated navigation is a popular approach to reduce this error. This study proposes an adaptive Kalman filter for adjusting the noise covariance of GNSS measurements under different positioning accuracies. The adaptive tuning is based on a proposed accuracy classification model trained by a supervised machine-learning method. First, principal component analysis is employed to identify the significant GNSS accuracy related features. Subsequently, the positioning accuracy model is trained based on a random forest learning algorithm with the labeled real GNSS dataset encompassing most scenarios concerning modern urban areas. To reduce the cases of misclassifying the GNSS accuracy, a fuzzy logic algorithm is employed to consider the GNSS accuracy propagation. Additionally, the process noise covariance of the INS is determined using the Allan variance analysis. The positioning performance of the proposed adaptive Kalman filter is compared with both a conventional Kalman filter and the positioning solution provided by the commercial flight control system, Pixhawk 2. The results show that the proposed adaptive Kalman filter using random forest with fuzzy logic can achieve a better classification of GNSS accuracy compared to the others. The overall positioning result improved by approximately 50% compared with the onboard solution.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationAerospace science and technology, Sept. 2018, v. 80, p. 368-380en_US
dcterms.isPartOfAerospace science and technologyen_US
dcterms.issued2018-09-
dc.identifier.eissn1626-3219en_US
dc.description.validate201902 bcrcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0287-n03en_US
dc.description.pubStatusPublisheden_US
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