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 mean absolute errors (MAEs) being 1.510 (m) and 1.077 (m) for self- and semi-supervised learning cases, respectively.

中文翻译：

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用于 WiFi 定位的移动诱导图学习


基于智能手机的用户移动跟踪可以有效地找到他/她的位置，但由于内置惯性测量单元 (IMU) 的低规格，其中不可预测的错误拒绝其独立使用，而是需要集成到另一种定位技术（如 WiFi）定位。本文旨在提出一种使用称为移动性诱导图学习（MINGLE）的图神经网络的新型集成技术，该技术是基于通过捕获不同用户移动特征而制成的两种类型的图而设计的。具体来说，将顺序测量点 (MP) 视为节点，用户的常规移动模式允许我们将相邻 MP 作为边连接，称为时间驱动移动图 (TMG)。其次，当用户从一个位置移动到另一个位置时，可以通过连接每条路径上的节点来捕获用户以恒定速度相对直线的过渡，称为方向驱动移动图（DMG）。然后，我们可以设计基于图卷积网络（GCN）的跨图学习，其中 TMG 和 DMG 的两个不同的 GCN 模型通过提供由 WiFi RTT 创建的不同输入特征并共享其权重来联合训练。此外，损失函数包括移动正则化项，使得由于用户稳定的移动速度，相邻位置估计之间的差异应该较小。注意到正则化项不需要真实位置，MINGLE 可以在半监督和自监督学习框架下设计。所提出的 MINGLE 的有效性通过现场实验得到了广泛验证，显示出比基准更好的定位精度，例如自监督学习和半监督学习案例的平均绝对误差 (MAE) 分别为 1.510 (m) 和 1.077 (m)。