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Using Dimensionality Reduction Techniques for Refining Passive Indoor Positioning Systems Based on Radio Fingerprinting

机译：使用降维技术完善基于无线电指纹的被动式室内定位系统

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摘要

Indoor positioning methods based on fingerprinting and radio signals rely on the quality of the radio map. For example, for room-level classification purposes, it is required that the signal observations related to each room exhibit significant differences in their RSSI values. However, it is difficult to verify and visualize that separability since radio maps are constituted by multi-dimensional observations whose dimension is directly related to the number of access points or monitors being employed for localization purposes. In this paper, we propose a refinement cycle for passive indoor positioning systems, which is based on dimensionality reduction techniques, to evaluate the quality of a radio map. By means of these techniques and our own data representation, we have defined two different visualization methods to obtain graphical information about the quality of a particular radio map in terms of overlapping areas and outliers. That information will be useful to determine whether new monitors are required or some existing ones should be moved. We have performed an exhaustive experimental analysis based on a variety of different scenarios, some deployed by our own research group and others corresponding to a well-known existing dataset widely analyzed by the community, in order to validate our proposal. As we will show, among the different combinations of data representation methods and dimensionality reduction techniques that we discuss, we have found that there are some specific configurations that are more useful in order to perform the refinement process.
机译：基于指纹和无线电信号的室内定位方法取决于无线电地图的质量。例如，出于房间级别分类的目的，要求与每个房间相关的信号观测在其RSSI值上显示出显着差异。但是，由于无线电地图是由多维观测构成的，因此很难验证和可视化这种可分离性，多维观测的维度与用于定位目的的接入点或监视器的数量直接相关。在本文中，我们提出了一种基于降维技术的无源室内定位系统的优化周期，以评估无线电地图的质量。通过这些技术和我们自己的数据表示，我们定义了两种不同的可视化方法，以便根据重叠区域和异常值来获取有关特定无线电地图质量的图形信息。该信息将有助于确定是需要新的监视器还是应移动一些现有监视器。为了验证我们的建议，我们已经根据各种不同的情况进行了详尽的实验分析，其中一些由我们自己的研究小组部署，而另一些则与社区广泛分析的知名现有数据集相对应。正如我们将要展示的，在我们讨论的数据表示方法和降维技术的不同组合中，我们发现有一些特定的配置对执行优化过程更有用。

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