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Fakultät für Elektrotechnik und Informationstechnik

Best Paper of the Best Paper Session @ 11th International Conference on Indoor Positioning and Indoor Navigation (IPIN) 2021

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Image of the award winner and of the award © CNI

We are delighted to announce that the contribution “Accurate Multi-Zone UWB TDOA Localization utilizing Cascaded Wireless Clock Synchronization” by Johannes Friedrich, Janis Tiemann and Christian Wietfeld, was awarded as the Best Paper of the Best Paper Session in the 11th International Conference on Indoor Positioning and Indoor Navigation (IPIN) 2021. The conference with a focus on wireless localization co-sponsored by several IEEE chapters, took place in December in Lloret de Mar, Spain. Within the context of the BMBF-funded project CELIDON (Support of rescue forces through localization in training and rescue operations) as well as the DFG Cooperative Research Centre 876, the research presented in this paper demonstrated methods of improving localization accuracy in highly challenging multi-zone environments such as a firefighter training facility in Dortmund.

A preprint of the authors' version is accessible here, but will also be available in the coming weeks in the IEEE Xplore electronic proceedings. Please see also the video of the project CELIDON here. More information about the Cooperative Research Centre 876, sub project A4, can be found here.


The high popularity of ultra-wideband technology for accurate indoor positioning in industrial and public spaces has led to a large amount of research in recent years. The focus has mostly been on localization accuracy in small-scale setups with a single localization zone. However, solutions designed for line-of-sight environments are not suitable for many large-scale applications. To overcome this lack of research, we propose novel concepts for precise wireless multi-hop clock synchronization and localization zone selection. By integration into a time-difference-of-arrival-based ultra-wideband localization scheme, continuous cross-spatial positioning in large-scale scenarios is enabled. Validation is carried out in an unprecedented testbed with multiple rooms. We could show that positioning accuracy in multi-room scenarios is up to three times higher when exploiting the proposed concepts compared to the initial accuracy achieved with existing approaches. In addition, we provide an open-source implementation of our real-time localization system.