Support Vector Machine for Determining Euler Angles in an Inertial Navigation System
This work addresses accuracy enhancement for inertial navigation systems, particularly in noisy MEMS sensor environments, but appears incremental as it applies existing ML methods to a specific domain.
The paper tackled improving the accuracy of an inertial navigation system using MEMS sensors by applying machine learning methods, specifically a Support Vector Machine, to classify Euler angles and achieved good classification results with optimal hyperparameters in noisy conditions.
The paper discusses the improvement of the accuracy of an inertial navigation system created on the basis of MEMS sensors using machine learning (ML) methods. As input data for the classifier, we used infor-mation obtained from a developed laboratory setup with MEMS sensors on a sealed platform with the ability to adjust its tilt angles. To assess the effectiveness of the models, test curves were constructed with different values of the parameters of these models for each core in the case of a linear, polynomial radial basis function. The inverse regularization parameter was used as a parameter. The proposed algorithm based on MO has demonstrated its ability to correctly classify in the presence of noise typical for MEMS sensors, where good classification results were obtained when choosing the optimal values of hyperpa-rameters.