Ultra-Wideband (UWB) is a Radio Frequency technology that is currently used for accurate indoor localization. However, the cost of deploying such a system is large, mainly due to the need for manually measuring the exact location of the installed infrastructure devices (“anchor nodes”). Self-calibration of UWB reduces deployment costs, because it allows for automatic updating of the coordinates of fixed nodes when they are installed or moved. Additionally, installation costs can also be reduced by using collaborative localization approaches where mobile nodes act as anchors. This article surveys the most significant research that has been done on self-calibration and collaborative localization. First, we find that often these terms are improperly used, leading to confusion for the readers. Furthermore, we find that in most of the cases, UWB-specific characteristics are not exploited, so crucial opportunities to improve performance are lost. Our classification and analysis provide the basis for further research on self-calibration and collaborative localization in the deployment of UWB indoor localization systems. Finally, we identify several research tracks that are open for investigation and can lead to better performance, e.g., machine learning and optimized physical settings.

中文翻译：

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超宽带定位系统的自校准和协同定位

超宽带 (UWB) 是一种射频技术，目前用于精确的室内定位。然而，部署这样一个系统的成本很大，主要是由于需要手动测量已安装基础设施设备（“锚节点”）的确切位置。UWB 的自校准降低了部署成本，因为它允许在安装或移动固定节点时自动更新它们的坐标。此外，还可以通过使用移动节点充当锚点的协作定位方法来降低安装成本。本文调查了在自我校准和协作本地化方面所做的最重要的研究。首先，我们发现这些术语经常被不当使用，导致读者混淆。此外，我们发现在大多数情况下，未利用 UWB 特有的特性，因此失去了提高性能的关键机会。我们的分类和分析为进一步研究超宽带室内定位系统部署中的自校准和协同定位提供了基础。最后，我们确定了几个可供调查的研究方向，可以带来更好的性能，例如机器学习和优化的物理设置。