A smartphone-based user mobility tracking could be effective in finding
his/her location, while the unpredictable error therein due to low
specification of built-in inertial measurement units (IMUs) rejects its
standalone usage but demands the integration to another positioning technique
like WiFi positioning. This paper aims to propose a novel integration technique
using a graph neural network called Mobility-INduced Graph LEarning (MINGLE),
which is designed based on two types of graphs made by capturing different user
mobility features. Specifically, considering sequential measurement points
(MPs) as nodes, a user's regular mobility pattern allows us to connect neighbor
MPs as edges, called time-driven mobility graph (TMG). Second, a user's
relatively straight transition at a constant pace when moving from one position
to another can be captured by connecting the nodes on each path, called a
direction-driven mobility graph (DMG). Then, we can design graph convolution
network (GCN)-based cross-graph learning, where two different GCN models for
TMG and DMG are jointly trained by feeding different input features created by
WiFi RTTs yet sharing their weights. Besides, the loss function includes a
mobility regularization term such that the differences between adjacent
location estimates should be less variant due to the user's stable moving pace.
Noting that the regularization term does not require ground-truth location,
MINGLE can be designed under semi- and self-supervised learning frameworks. The
proposed MINGLE's effectiveness is extensively verified through field
experiments, showing a better positioning accuracy than benchmarks, say root
mean square errors (RMSEs) being 1.398 (m) and 1.073 (m) for self- and
semi-supervised learning cases, respectively.

利用移动智能手机进行用户位置追踪，在低规格的惯性测量单元（IMUs）内存在无法预测的错误，因此不能单独使用，而需要与另一种定位技术（如 WiFi 定位）进行集成。本文提出了一种新的集成技术，使用名为 Mobility-INduced Graph LEarning（MINGLE）的图神经网络，它基于捕捉不同用户移动特征生成的两种类型的图。经过实地实验验证，MINGLE 相比基准模型显示出更好的定位精度，半监督和自监督学习情况下的均方根误差（RMSE）分别为 1.398 米和 1.073 米。