3D Electromagnetic Positioning Optimization by Means of Deep Learning

Abstract

The 3D electromagnetic positioning system consists of four generating coils and three-axis magnetic sensor, accelerometer, and gyroscope in magnetic field. These systems are generally used in navigation, ballistic missile tracking, medicine, robotics, biomechanics, and education. Electromagnetic positioning can be performed in a limited volume. In addition, there are errors in the position calculation. In this study, the aim is to increase the coverage volume and to minimize the errors in the sensor position. Therefore, large radius coil, high circuit current, and high number turn of coil were used to increase the working volume. By optimizing, the sensor was moved closest to the actual position. In order to reduce these errors different software and algorithms were used. Some of them are Levenberg-Marquardt, artificial neural networks, etc. In this study, deep learning algorithms, which are a more advanced version of machine learning concept, are used. Deep networks can be thought of as a special case of multi-layered classical artificial neural networks. Mean square error (MSE) was used for performance analysis of the system.