Accuracy enhancement of Wi-Fi positioning by ranging in dense UWB networks*

This study proposed a fusion-based approach of Wi-Fi fingerprint positioning with relatively accurate UWB ranging in dense wireless networks (DWNs) to optimize indoor localization accuracy. Such an optimization was achieved through a proposed objective or loss function that minimizes the total positioning errors of the nodes composing a virtual geometric structure with the internode distances preserved during optimization in the DWN. Theoretical analysis and simulation results of DWNs up to 100 nodes revealed that, with at least 6 extra measured distances among as few as 4 participating nodes including the device-under-targeting (DUT) itself, the resulting localization accuracy improvement was proportional to the square root of the number of participating nodes towards the limit of the ranging error. Per experiment outcomes of a 10-node UWB DWN, our Wi-Fi/UWB ensemble reduced the baseline Wi-Fi K-nearest neighbors (KNN) fingerprint localization error by up to 69.8 %, further validating our theoretical and simulation results.

Automated summary

This study proposes a fusion-based approach that combines Wi-Fi fingerprint positioning with accurate UWB ranging to optimize indoor localization accuracy in dense wireless networks (DWNs). Theoretical analysis and simulation results show that the improvement in localization accuracy is proportional to the square root of the number of participating nodes when at least 6 additional measured distances are available.