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Artificial neural network for tilt compensation in yaw estimation

dc.contributor.authorHalitim, Ali mounir
dc.contributor.authorBouhedda, Mounir
dc.date.accessioned2024-12-12T11:38:48Z
dc.date.available2024-12-12T11:38:48Z
dc.date.issued2023-12-21
dc.description.abstractnputs. The dataset for training and testing the ANN was collected based on a 3D nonmagnetic scaled platform, using a low-cost IMU and a Raspberry Pi platform. On one hand, our method outperforms the conventional tilt-compensation technique and other complementary filters (Madgwick and Mahony) in terms of accuracy, as evidenced by the root mean square error (RMSE = 1.95 ). However, this superiority comes at the expense of a more complex system that consumes more processing timefr_FR
dc.identifier.citationTechnologiefr_FR
dc.identifier.govdocA75.55
dc.identifier.urihttps://dspace.univ-medea.dz/handle/123456789/12528
dc.language.isoenfr_FR
dc.subjectMachine learningfr_FR
dc.subjectArtificial Neural Networkfr_FR
dc.subjectArtificial intelligencefr_FR
dc.subjectinertial measurementfr_FR
dc.subjectmicro–electro–mechanical systemsfr_FR
dc.subjecttilt compensationfr_FR
dc.subjectyaw estimationfr_FR
dc.titleArtificial neural network for tilt compensation in yaw estimationfr_FR
dc.typeArticlefr_FR

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