论文标题
对于无设备的室内本地化，几乎没有射击的转移学习
Few-Shot Transfer Learning for Device-Free Fingerprinting Indoor Localization
论文作者
论文摘要
无设备的无线室内定位是物联网（IoT）的重要技术，基于指纹的方法被广泛使用。基于指纹的方法的一个普遍挑战是数据收集和标签。本文提出了一个几次传输学习系统，该系统仅使用当前环境中的少量标记数据，并重新恢复其他以前在其他环境中收集的现有标记数据，从而大大降低了每个新环境中本地化的数据收集和标记成本。核心方法在于基于图形神经网络（GNN）的几个射击传输学习及其修改。在现实世界环境上进行的实验结果表明，所提出的系统与卷积神经网络（CNN）模型的性能相当，标记数据较少40倍。
Device-free wireless indoor localization is an essential technology for the Internet of Things (IoT), and fingerprint-based methods are widely used. A common challenge to fingerprint-based methods is data collection and labeling. This paper proposes a few-shot transfer learning system that uses only a small amount of labeled data from the current environment and reuses a large amount of existing labeled data previously collected in other environments, thereby significantly reducing the data collection and labeling cost for localization in each new environment. The core method lies in graph neural network (GNN) based few-shot transfer learning and its modifications. Experimental results conducted on real-world environments show that the proposed system achieves comparable performance to a convolutional neural network (CNN) model, with 40 times fewer labeled data.
