  The continuously increasing bandwidth and antenna aperture available in
wireless networks laid the foundation for developing competitive positioning
solutions relying on communications standards and hardware. However, poor
propagation conditions such as non-line of sight (NLOS) and rich multipath
still pose many challenges due to outlier measurements that significantly
degrade the positioning performance. In this work, we introduce an iterative
positioning method that reweights the time of arrival (ToA) and angle of
arrival (AoA) measurements originating from multiple locators in order to
efficiently remove outliers. In contrast to existing approaches that typically
rely on a single locator to set the time reference for the time difference of
arrival (TDoA) measurements corresponding to the remaining locators, and whose
measurements may be unreliable, the proposed iterative approach does not rely
on a reference locator only. The resulting robust position estimate is then
used to initialize a computationally efficient gradient search to perform
maximum likelihood position estimation. Our proposal is validated with an
experimental setup at 3.75 GHz with 5G numerology in an indoor factory
scenario, achieving an error of less than 50 cm in 95% of the measurements. To
the best of our knowledge, this paper describes the first proof of concept for
5G-based joint ToA and AoA localization.
