4.7 Article

Self-calibration and Collaborative Localization for UWB Positioning Systems: A Survey and Future Research Directions

Journal

ACM COMPUTING SURVEYS
Volume 54, Issue 4, Pages -

Publisher

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3448303

Keywords

Survey; indoor localization; ultra-wideband; self-calibration; collaborative localization

Funding

  1. VLAIO proeftuinproject SmartConnectivity
  2. imec.icon InWareDrones
  3. EOS project MUSE-WINET

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This article discusses the application of Ultra-Wideband technology in indoor localization and highlights the benefits of self-calibration and collaborative localization in reducing deployment costs. However, issues such as improper usage of terms and underutilization of UWB-specific characteristics hinder performance improvement. Further research tracks include machine learning and optimized physical settings.
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.

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