  In this paper, we study probabilistic time-of-arrival (ToA) and
angle-of-arrival (AoA) joint localization in real indoor environments. To
mitigate the effects of multipath propagation, the joint localization algorithm
incorporates into the likelihood function Gaussian mixture models (GMM) and the
Von Mises-Fisher distribution to model time bias errors and angular
uncertainty, respectively. We evaluate the algorithm performance using a
proprietary prototype deployed in an indoor factory environment with
infrastructure receivers in each of the four corners at the ceiling of a 10
meter by 20 meter section. The field test results show that our joint
probabilistic localization algorithm significantly outperforms baselines using
only ToA or AoA measurements and achieves 2-D sub-meter accuracy at the
90%-ile. We also numerically demonstrate that the joint localization algorithm
is more robust to synchronization errors than the baseline using ToA
measurements only.
