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.

中文翻译：

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复杂磁环境中基于一维卷积神经网络（CNN）的高精度磁辅助航向角计算方法。

由于其广泛存在并且不受人造信号的影响，地磁场信息在室内行人导航系统中的应用引起了研究人员的广泛关注。但是，对于室内环境，复杂的磁场会严重干扰地磁场信号，从而导致磁辅助导航系统的定位精度降低。因此，迫切需要一种筛选出不受干扰的地磁场数据以实现室内高精度行人惯性导航的方法。在本文中，我们提出了一种基于一维卷积神经网络（1D CNN）的算法来筛选磁场数据。通过将特定时间窗口内的磁数据编码为时间序列，具有两个卷积层的一维CNN旨在提取数据特征。为了避免由人工标记引起的错误，特征向量将在特征空间中聚类，以使用无监督方法对磁性数据进行分类。实验结果表明，该方法能够很好地区分地磁场数据与室内扰动磁场数据，进一步提高了航向角的计算精度。我们的工作为实现高精度室内行人导航系统提供了一条可能的技术途径。实验结果表明，该方法能够很好地区分地磁场数据与室内扰动磁场数据，进一步提高了航向角的计算精度。我们的工作为实现高精度室内行人导航系统提供了一条可能的技术途径。实验结果表明，该方法能够很好地区分地磁场数据与室内扰动磁场数据，进一步提高了航向角的计算精度。我们的工作为实现高精度室内行人导航系统提供了一条可能的技术途径。