室内环境下概率ToA和AoA联合定位的实验研究
Experimental Study on Probabilistic ToA and AoA Joint Localization in Real Indoor Environments
摘要：
在本文中，我们研究了概率到达时间（ToA）和到达角（AoA）联合定位在真实的室内环境。为了减轻多径传播的影响，联合定位算法结合到似然函数高斯混合模型（GMM）和Von Mises-Fisher分布模型的时间偏差误差和角度的不确定性，分别。我们评估的算法性能使用专有的原型部署在室内工厂环境中的基础设施接收器在每个四个角落的天花板上的10米乘20米的部分。现场测试结果表明，我们的联合概率定位算法显着优于基线，仅使用ToA或AoA测量，并达到2-D亚米级精度在90%-ile。我们还数值证明，联合定位算法是更强大的同步误差比基线使用ToA测量。
英文摘要：
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.
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