论文标题

具有强大的全球渠道图表的室内本地化:基于时间距离的方法

Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach

论文作者

Stahlke, Maximilian, Yammine, George, Feigl, Tobias, Eskofier, Bjoern M., Mutschler, Christopher

论文摘要

基于指纹的定位显着改善了非视线主导区域的室内定位性能。但是,它的部署和维护是成本密集的,因为它需要基本真相的参考系统来进行初始培训和对环境变化的适应。相比之下,通道图(CC)在没有明确参考信息的情况下起作用,仅需要通道状态信息(CSI)的空间相关性。尽管CC在对无线电环境的几何形状进行建模时表现出了令人鼓舞的结果,但使用多锚大宽带测量值对CC进行更深入的了解仍在待处理。我们为时间同步的单输入/单输出CSI贡献了新的距离度量,该指标接近与欧几里得距离的线性相关性。这允许在没有注释的情况下学习环境的全球几何形状。为了有效地优化全球通道图,我们使用暹罗神经网络近似度量。这只能使用从图表到现实世界坐标的线性转换才能完整的CC辅助指纹和定位。我们将我们的方法与5G和UWB无线电设置记录的两个不同的现实世界数据集进行了比较CC的最新方法。我们的方法以UWB的本地化精度为0.69m,而5G设置的本地化精度优于其他方法。我们表明,CC辅助指纹识别可以使高度准确的定位并减少(或消除)注释训练数据。

Fingerprinting-based positioning significantly improves the indoor localization performance in non-line-of-sight-dominated areas. However, its deployment and maintenance is cost-intensive as it needs ground-truth reference systems for both the initial training and the adaption to environmental changes. In contrast, channel charting (CC) works without explicit reference information and only requires the spatial correlations of channel state information (CSI). While CC has shown promising results in modelling the geometry of the radio environment, a deeper insight into CC for localization using multi-anchor large-bandwidth measurements is still pending. We contribute a novel distance metric for time-synchronized single-input/single-output CSIs that approaches a linear correlation to the Euclidean distance. This allows to learn the environment's global geometry without annotations. To efficiently optimize the global channel chart we approximate the metric with a Siamese neural network. This enables full CC-assisted fingerprinting and positioning only using a linear transformation from the chart to the real-world coordinates. We compare our approach to the state-of-the-art of CC on two different real-world data sets recorded with a 5G and UWB radio setup. Our approach outperforms others with localization accuracies of 0.69m for the UWB and 1.4m for the 5G setup. We show that CC-assisted fingerprinting enables highly accurate localization and reduces (or eliminates) the need for annotated training data.

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