Abstract

Ultra wideband positioning systems typically use techniques such as two way ranging (TWR) or time difference of arrival (TDoA) to calculate the position of mobile tags. TDoA techniques require the transmission of only a single packet by the mobile tag, thus providing better scalability, higher update rates and less energy consumption than TWR techniques. However, the TDoA performance degrades heavily when a subset of the anchors are in non-line-of-sight (NLOS) conditions with the tag or with each other. To remedy this, we propose and compare different algorithms to select a subset of anchor pairs before calculating the TDoA position in 3 different conditions: LOS conditions between all devices, NLOS conditions between tag and anchor nodes and NLOS conditions between anchors and between tag and anchor nodes. We use an experimental setup with 1 tag and 8 anchor nodes to compare the accuracy gains obtained by using both simple algorithms and more complex machine learning (ML) based algorithms applied on the channel impulse responses of anchor pairs. By selecting the best anchor combinations our algorithms can reduce the positioning error by 75% (assuming perfectly known ground truth), by 19% using realistically low complexity algorithms and by 38% for ML based algorithms.

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