In indoor positioning, signal fluctuation is highly location-dependent. However, signal uncertainty is one critical yet commonly overlooked dimension of the radio signal to be fingerprinted. This paper reviews the commonly used Gaussian Processes (GP) for probabilistic positioning and points out the pitfall of using GP to model signal fingerprint uncertainty. This paper also proposes Deep Gaussian Processes (DGP) as a more informative alternative to address the issue. How DGP better measures uncertainty in signal fingerprinting is evaluated via simulated and realistically collected datasets.

翻译：在室内定位中,信号波动高度取决于位置,然而,信号不确定性是需要指纹识别的无线电信号中一个关键但通常被忽视的方面。本文回顾了常用高斯进程(GP)的概率定位,并指出了使用GP模拟指纹不确定性的缺陷。本文还提出了深海高斯进程(DGP)作为解决这一问题的更知情的替代方法。DGP如何更好地衡量信号指纹识别的不确定性,通过模拟和现实收集的数据集进行评估。