论文标题
多视差分深度学习，并应用于可扩展的室内本地化
Multiview Variational Deep Learning with Application to Scalable Indoor Localization
论文作者
论文摘要
用许多接收器测量的无线电通道状态信息（CSI）是通过机器学习与判别模型本地化设备的良好资源。但是，当无线电图复杂时，例如在建筑走廊中，CSI定位是不平凡的。本文介绍了使用WiFi的CSI进行室内定位的视图选择性深度学习（VSDL）系统。从多个访问点（AP）获得的CSI的多视培训在监督的变异深网上生成潜在功能。然后将此信息应用于其他网络以进行主导视图分类，以增强本地化的回归准确性。由于拒绝了来自多个视图的非信息性潜在特征，因此我们可以达到0.77 m的本地化准确性，在实际建筑环境中，在实际应用中，最著名的准确性优于30 $ \％$。据我们所知，这是应用变分推断并构建可伸缩系统进行无线电定位的第一种方法。此外，我们的工作还研究了一种通过多视图数据共存的多视图数据的监督学习方法。
Radio channel state information (CSI) measured with many receivers is a good resource for localizing a transmit device with machine learning with a discriminative model. However, CSI localization is nontrivial when the radio map is complicated, such as in building corridors. This paper introduces a view-selective deep learning (VSDL) system for indoor localization using CSI of WiFi. The multiview training with CSI obtained from multiple groups of access points (APs) generates latent features on supervised variational deep network. This information is then applied to an additional network for dominant view classification to enhance the regression accuracy of localization. As non-informative latent features from multiple views are rejected, we can achieve a localization accuracy of 0.77 m, which outperforms by 30 $\%$ the best known accuracy in practical applications in a real building environment. To the best of our knowledge, this is the first approach to apply variational inference and to construct a scalable system for radio localization. Furthermore, our work investigates a methodology for supervised learning with multiview data where informative and non-informative views coexist.
