Artificial neural network for tilt compensation in yaw estimation
| dc.contributor.author | Halitim, Ali mounir | |
| dc.contributor.author | Bouhedda, Mounir | |
| dc.date.accessioned | 2024-12-12T11:38:48Z | |
| dc.date.available | 2024-12-12T11:38:48Z | |
| dc.date.issued | 2023-12-21 | |
| dc.description.abstract | nputs. 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 time | fr_FR |
| dc.identifier.citation | Technologie | fr_FR |
| dc.identifier.govdoc | A75.55 | |
| dc.identifier.uri | https://dspace.univ-medea.dz/handle/123456789/12528 | |
| dc.language.iso | en | fr_FR |
| dc.subject | Machine learning | fr_FR |
| dc.subject | Artificial Neural Network | fr_FR |
| dc.subject | Artificial intelligence | fr_FR |
| dc.subject | inertial measurement | fr_FR |
| dc.subject | micro–electro–mechanical systems | fr_FR |
| dc.subject | tilt compensation | fr_FR |
| dc.subject | yaw estimation | fr_FR |
| dc.title | Artificial neural network for tilt compensation in yaw estimation | fr_FR |
| dc.type | Article | fr_FR |
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