A.N. Grekov1,2, A.A. Kabanov2, S.Yu. Alekseev1
1Institute of Natural and Technical Systems, RF, Sevastopol, Lenin St., 28
2Sevastopol State University, RF, Sevastopol, Universitetskaya St., 33
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. The information obtained from the developed laboratory setup with MEMS sensors installed on a sealed platform with the ability to adjust its tilt angles and placed in an aquarium with water was used as input data for the classifier. To assess the effectiveness of the models, test curves were constructed with different values of the parameters of these models for three kernels: linear, polynomial, and radial-basis function. As a parameter, the inverse regularization parameter was used, a higher value of which corresponded to large penalties for errors, while choosing a lower value means less severity with respect to misclassification errors.
Applying the complex nonlinear architecture of the SVM-based machine learning method, which has been used in many applications, the ML algorithm is implemented to improve the determination of Euler angles (roll, pitch and yaw) from MEMS sensor data. For this, the problem of overfitting and undertraining is solved by choosing the optimal values of hyperparameters.
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
Further research will be directed to the application of other algorithms for the classification of ML, as well as to increase the types of input data.
Keywords: navigation, accelerometer, acceleration, gyroscope, magnetometer, error, autonomous underwater vehicles, machine learning, algorithm.
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