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Open AccessArticle

A High-Precision Magnetic-Assisted Heading Angle Calculation Method Based on a 1D Convolutional Neural Network (CNN) in a Complicated Magnetic Environment

by Guanghui Hu 1,2,3, Hong Wan 1,3,* and Xinxin Li 1,2,*
1
State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
2
School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
3
Vtran Tech (Chang Zhou) Co., Ltd., Shanghai 200135, China
*
Authors to whom correspondence should be addressed.
Micromachines 2020, 11(7), 642; https://doi.org/10.3390/mi11070642
Received: 14 June 2020 / Revised: 22 June 2020 / Accepted: 25 June 2020 / Published: 29 June 2020
(This article belongs to the Special Issue Inertial MEMS Devices)
Due to its widespread presence and independence from artificial signals, the application of geomagnetic field information in indoor pedestrian navigation systems has attracted extensive attention from researchers. However, for indoors environments, geomagnetic field signals can be severely disturbed by the complicated magnetic, leading to reduced positioning accuracy of magnetic-assisted navigation systems. Therefore, there is an urgent need for methods which screen out undisturbed geomagnetic field data for realizing the high accuracy pedestrian inertial navigation indoors. In this paper, we propose an algorithm based on a one-dimensional convolutional neural network (1D CNN) to screen magnetic field data. By encoding the magnetic data within a certain time window to a time series, a 1D CNN with two convolutional layers is designed to extract data features. In order to avoid errors arising from artificial labels, the feature vectors will be clustered in the feature space to classify the magnetic data using unsupervised methods. Our experimental results show that this method can distinguish the geomagnetic field data from indoors disturbed magnetic data well and further significantly improve the calculation accuracy of the heading angle. Our work provides a possible technical path for the realization of high-precision indoor pedestrian navigation systems. View Full-Text
Keywords: magnetic-assisted; one-dimensional convolutional neural network (1D CNN); magnetic anomaly detection; pedestrian inertial navigation magnetic-assisted; one-dimensional convolutional neural network (1D CNN); magnetic anomaly detection; pedestrian inertial navigation
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Hu, G.; Wan, H.; Li, X. A High-Precision Magnetic-Assisted Heading Angle Calculation Method Based on a 1D Convolutional Neural Network (CNN) in a Complicated Magnetic Environment. Micromachines 2020, 11, 642.

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