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A Bayesian Probabilistic Approach to Hybrid Localization with GNSS and LTE-OTDOA in Multipath Channels

机译：多径频道中GNSS和LTE-OTDOA的贝叶斯概率方法杂交定位

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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.
机译：对于到达时间（TOA）本地化，由无法解析的多径和非视线（NLOS）反射引入的信道偏差严重降低了性能。解决这一损害，Perez-Cruz等人。提出了一种贝叶斯概率方法，其特征具有概率分布的信道偏压，使得它可以鲁棒地补偿，并说明了其对长期演进（LTE）的有效性 - 观察到的到达时间差（OTDOA）定位。在这项工作中，我们通过全球导航卫星系统（GNSS）和LTE-OTDOA概括了这种贝叶斯概率方法来混合定位。在混合室内和户外场景中使用实际的超空气测量数据，我们证明基于一些强大的信道偏置分布，所提出的混合定位算法与考虑LTE-OTDOA或GNSS的概率算法相比，实现了更好的定位精度。它还使用众所周知的非线性最小二乘（NLS）技术显着优于基线混合定位算法。

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