Bayesian location estimation of mobile devices using a signal strength model

IF 1.8 Q2 GEOGRAPHY
M. Tennekes, Y. Gootzen
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引用次数: 2

Abstract

Mobile network operator (MNO) data are a rich data source for various topics in official statistics, such as present population, mobility, migration, and tourism. Estimating the geographic location of mobile devices is an essential step for statistical inference. Most studies use Voronoi tessellation for this, which is based on the assumption that mobile devices are always connected to the nearest radio cell. We propose an alternative location estimation method following a Bayesian approach and using a physical model for the received signal strength. Our Bayesian framework allows for different modules of prior knowledge about where devices are expected to be, and different modules for the likelihood of connection given a geographic location. We discuss and compare the use of several prior modules, including one that is based on land use. For the likelihood module we propose a signal strength model using radio cell properties such as antenna height, propagation direction, and power. Using Bayes' rule, we derive a posterior probability distribution that is an estimate of the geographic location, which can be used for further statistical inference. We describe the method and provide illustrations of a fictional example that resembles a real-world situation. The method has been implemented in the R packages mobloc and mobvis, which are briefly described.
使用信号强度模型的移动设备的贝叶斯位置估计
移动网络运营商(MNO)数据是官方统计中各种主题的丰富数据源,如现有人口、流动性、迁移和旅游。估计移动设备的地理位置是统计推断的重要步骤。大多数研究使用Voronoi镶嵌法,这是基于移动设备总是连接到最近的无线基站的假设。我们提出了一种替代的位置估计方法,该方法遵循贝叶斯方法并使用接收信号强度的物理模型。我们的贝叶斯框架允许不同的先验知识模块来预测设备的位置,以及不同的模块来预测给定地理位置的连接可能性。我们讨论和比较了几个先前模块的使用,包括一个基于土地使用的模块。对于似然模组,我们提出一个讯号强度模型,使用无线电单元的特性,例如天线高度、传播方向和功率。利用贝叶斯规则,我们得到了一个后验概率分布,它是对地理位置的估计,可以用于进一步的统计推断。我们描述了该方法,并提供了一个类似于现实世界情况的虚构示例的插图。该方法已在R软件包mobloc和mobvis中实现,并对其进行了简要描述。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.10
自引率
0.00%
发文量
5
审稿时长
9 weeks
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