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.

中文翻译：

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实际室内环境中概率ToA和AoA联合定位的实验研究

在本文中，我们研究了实际室内环境中的概率到达时间（ToA）和到达角（AoA）联合定位。为了减轻多径传播的影响，联合定位算法将似然函数高斯混合模型（GMM）和Von Mises-Fisher分布结合到了时间偏差误差和角度不确定性中。我们使用部署在室内工厂环境中的专有原型来评估算法性能，该原型在10米x 20米截面的天花板的四个角中的每个角处都有基础结构接收器。现场测试结果表明，仅使用ToA或AoA测量结果，我们的联合概率定位算法明显优于基线，并且在90％ile时达到了二维亚米精度。