Performance of a TDOA indoor positioning solution in real-world 5G network

Indoor localization is one of the most requested applications for 5G networks. With the continuous deployment of new 5G networks, the implementation of a reliable, robust, and accurate indoor positioning algorithm has become a widely and thoroughly researched topic. However, the solutions realized for real-world environments still do not meet all performance expectations. In this paper, a real-world 5G environment is investigated and the feasibility of the measurements collected for positioning is discussed. Based on these findings, a framework for indoor positioning is proposed and evaluated using Time Difference of Arrival (TDOA) measurements. Within the framework, an iterative algorithm is implemented to minimize a non-linear least squares function extended with four additional methods to mitigate the measurement errors caused mainly by non-line-of sight propagation. The best parameter settings are determined with hyperparameter optimization, and it is shown that with these settings, sub-meter positioning error is a viable goal in certain scenarios.