基于噪声到达角测量的射线追踪概率定位
Probabilistic Positioning Via Ray Tracing With Noisy Angle of Arrival Measurements
AI总结：
针对非视距场景，提出基于噪声到达角测量与射线追踪的定位方法，通过拟合高斯混合模型并融合多基站概率，实现低复杂度且鲁棒的UE定位。
摘要：
我们考虑非视距（NLoS）情况下的定位问题，其中多个基站（BS）试图基于上行到达角（AoA）测量和环境数字孪生来定位用户设备（UE）。根据AoA统计以蒙特卡洛方式进行射线发射，能够为每个基站生成一个点图。这些点代表射线在给定用户设备（UE）高度处与xy平面的交点。我们提出为每个点图拟合一个参数化概率密度函数（pdf），例如高斯混合模型（GMM）。将每个基站获得的pdf相乘，即可计算UE的位置概率。该方法产生了一种对发射射线数量减少具有鲁棒性的算法。此外，这些参数化pdf可以在离线阶段进行拟合和存储，从而在在线阶段避免射线追踪。这显著降低了定位方法的计算复杂度。
英文摘要：
We consider the positioning problem in non line-of-sight (NLoS) situations, where several base stations (BS) try to locate a user equipment (UE) based on uplink angle of arrival (AoA) measurements and a digital twin of the environment. Ray launching in a Monte Carlo manner according to the AoA statistics enables to produce a map of points for each BS. These points represent the intersections of the rays with a xy plane at a given user equipment (UE) elevation. We propose to fit a parametric probability density function (pdf), such as a Gaussian mixture model (GMM), to each map of points. Multiplying the obtained pdfs for each BS enables to compute the position probability of the UE. This approach yields an algorithm robust to a reduced number of launched rays. Moreover, these parametric pdfs may be fitted and stored in an offline phase such that ray tracing can be avoided in the online phase. This significantly reduces the computational complexity of the positioning method.
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