Cellular network positioning is a mandatory requirement for localizing emergency callers, such as E911 in North America. Although smartphones are normally equipped with GPS modules, there are still a large number of users with cell phones only as basic devices, and GPS could be ineffective in urban canyon environments. To this end, the RF fingerprints based positioning mechanism is incorporated into LTE architecture by 3GPP, where the major challenge is to collect geo-tagged RF fingerprints in vast areas. This paper proposes to utilize the subspace identification approach for large-scale RF fingerprints prediction. We formulate the problem into the problem of finding the optimal subspace over Stiefel manifold, and redesign the Stiefel-manifold optimization method with fast convergence rate. Moreover, we propose a sliding window mechanism for the practical large-scale fingerprints prediction scenario, where recorded fingerprints are unevenly distributed in the vast area. Combining the two proposed mechanisms enables an efficient method of large-scale fingerprints prediction in the city level. Further, we validate our theoretical analysis and proposed mechanisms by conducting experiments with real mobile data, which shows that the resulted localization accuracy and reliability with our predicted fingerprints exceed the requirement of E911.

中文翻译：

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蜂窝网络定位的射频指纹预测：一种子空间识别方法

蜂窝网络定位是定位紧急呼叫者的强制性要求，例如北美的 E911。虽然智能手机通常都配备了 GPS 模块，但仍有大量用户仅将手机作为基本设备，GPS 在城市峡谷环境中可能无法发挥作用。为此，3GPP 将基于 RF 指纹的定位机制纳入 LTE 架构，其中主要挑战是在大范围内收集带有地理标记的 RF 指纹。本文提出利用子空间识别方法进行大规模射频指纹预测。我们将问题表述为在 Stiefel 流形上寻找最优子空间的问题，并重新设计具有快速收敛速度的 Stiefel-流形优化方法。而且，我们为实际的大规模指纹预测场景提出了一种滑动窗口机制，其中记录的指纹在广阔的区域中分布不均。结合这两种提出的机制，可以提供一种在城市层面进行大规模指纹预测的有效方法。此外，我们通过对真实移动数据进行实验来验证我们的理论分析和提出的机制，这表明我们预测指纹的定位精度和可靠性超过了 E911 的要求。