Abstract
For time-of-arrival (TOA) localization, the channel bias introduced by unresolvable multipath and non-line-of-sight (NLOS) reflections severely degrades the performance. To address this impairment, Perez-Cruz et al. proposed a Bayesian probabilistic approach, which characterizes the channel bias with a probability distribution such that it can be robustly compensated, and illustrated its effectiveness for Long Term Evolution (LTE) - observed time difference of arrival (OTDOA) positioning. In this work, we generalize this Bayesian probabilistic approach to hybrid positioning with both global navigation satellite system (GNSS) and LTE-OTDOA. Using actual over-the-air measurement data in mixed indoor and outdoor scenarios, we demonstrate that based on some robust channel bias distributions the proposed hybrid localization algorithm achieves better positioning accuracy compared with the probabilistic algorithm considering LTE-OTDOA or GNSS only. It also significantly outperforms a baseline hybrid positioning algorithm using the well-known nonlinear least squares (NLS) techniques.
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