利用高斯过程深入测量信号指纹的不确定度
Measuring Uncertainty in Signal Fingerprinting with Gaussian Processes Going Deep
摘要：
在室内定位中，信号波动高度依赖于位置。然而，信号的不确定性是要进行指纹识别的无线电信号的一个关键但通常被忽视的维度。本文回顾了常用的高斯过程（GP）的概率定位，并指出使用GP模型信号指纹的不确定性的陷阱。本文还提出了深度高斯过程（DGP）作为解决这个问题的一个更有用的替代方案。DGP如何更好地测量信号指纹的不确定性，通过模拟和现实收集的数据集进行评估。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
