Support Vector Machine for Determining Euler Angles in an Inertial Navigation System

TL;DR

This paper presents a machine learning approach using SVMs to accurately determine Euler angles in inertial navigation systems with MEMS sensors, improving robustness against sensor noise.

Contribution

It introduces a novel SVM-based classification method optimized for noisy MEMS sensor data to enhance inertial navigation accuracy.

Findings

Effective classification with optimal hyperparameters

Robustness against typical MEMS sensor noise

Improved accuracy of Euler angle estimation

Abstract

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.