In recent years, the use of WiFi fingerprints for indoor positioning has grown in popularity, largely due to the widespread availability of WiFi and the proliferation of mobile communication devices. However, many existing methods for constructing fingerprint datasets rely on labor-intensive and time-consuming processes of collecting large amounts of data. Additionally, these methods often focus on ideal laboratory environments, rather than considering the practical challenges of large multi-floor buildings. To address these issues, we present a novel WiDAGCN model that can be trained using a small number of labeled site survey data and large amounts of unlabeled crowdsensed WiFi fingerprints. By constructing heterogeneous graphs based on received signal strength indicators (RSSIs) between waypoints and WiFi access points (APs), our model is able to effectively capture the topological structure of the data. We also incorporate graph convolutional networks (GCNs) to extract graph-level embeddings, a feature that has been largely overlooked in previous WiFi indoor localization studies. To deal with the challenges of large amounts of unlabeled data and multiple data domains, we employ a semi-supervised domain adversarial training scheme to effectively utilize unlabeled data and align the data distributions across domains. Our system is evaluated using a public indoor localization dataset that includes multiple buildings, and the results show that it performs competitively in terms of localization accuracy in large buildings.

翻译：近年来，基于WiFi指纹的室内定位方法因WiFi的广泛部署和移动通信设备的普及而日益流行。然而，现有指纹数据集构建方法大多依赖人工密集且耗时的海量数据采集过程。此外，这些方法通常关注理想实验室环境，而非大型多楼层建筑的实际挑战。为解决这些问题，我们提出一种新型WiDAGCN模型，该模型可通过少量标记现场勘测数据和大量无标签群智感知WiFi指纹进行训练。通过构建基于路径点与WiFi接入点（AP）间接收信号强度指示（RSSI）的异构图，模型能够有效捕获数据的拓扑结构。我们还引入图卷积网络（GCN）提取图级嵌入，这一特征在以往WiFi室内定位研究中常被忽视。为应对大量无标签数据和多数据域的挑战，我们采用半监督域对抗训练方案，以有效利用无标签数据并跨域对齐数据分布。该系统使用包含多栋建筑的公共室内定位数据集进行评测，结果显示其在大型建筑中具有竞争力的定位精度。