论文标题
支持向量机，用于确定惯性导航系统中的Euler角度
Support Vector Machine for Determining Euler Angles in an Inertial Navigation System
论文作者
论文摘要
本文讨论了使用机器学习（ML）方法基于MEMS传感器创建的惯性导航系统的准确性的提高。作为分类器的输入数据，我们使用了从已发达的实验室设置获得的Infor-Mation，该实验室设置在密封平台上具有MEMS传感器，能够调整其倾斜角度。为了评估模型的有效性，在线性的多项式径向基础函数的情况下，用这些模型的参数的不同值构建了测试曲线。反正则参数用作参数。提出的基于MO的算法已经证明了其在MEMS传感器典型的噪声存在下正确分类的能力，在选择HyperPA-RameTers的最佳值时，获得了良好的分类结果。
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.
