利用惯性传感器改进稀疏有机WiFi定位

Johannes Schmid, Tobias Gädeke, Dorothy W. Curtis, J. Ledlie
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引用次数: 7

摘要

近年来,建筑内部的个人位置发现与导航成为一个重要的研究课题。根据移动设备确定当前位置的一种方法是将可用的WiFi接入点(ap)集合(即给定空间的指纹)与先前收集的数据库进行比较。在此背景下,本文解决了此类系统需要事先建立指纹数据库的固有问题。因此,可能会出现建筑物在数据库中仅部分表示,并且只能在建筑物空间的子集中提供本地化的情况。这个问题尤其出现在用户连续贡献位置绑定的众包(有机)方法中。在这些情况下,需要一个额外的系统来提供本地化。我们首次研究了来自惯性传感器的行人航位推算(PDR)与来自WiFi定位系统的位置估计的融合。我们概述了一种可能的粒子滤波器设计,并在实验数据上分析了它的性能。我们的结论是,概述的方法可以帮助提高WiFi定位,并在众包环境中特别有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving sparse organic WiFi localization with inertial sensors
Personal location discovery and navigation within buildings has become an important research topic in the last years. One method to determine one's current position based on mobile-devices is to compare the set of available WiFi access points (APs), i.e. the fingerprint of a given space, to a previously collected database. In this context, this paper addresses the inherent problem of such systems that this fingerprint database needs to be established beforehand. Thus, situations can occur where a building is only partially represented in the database and localization can only be provided in a subset of the spaces of the building. This problem occurs especially in crowd-sourcing (organic) approaches where users consecutively contribute location-binds. In these situations an additional system is needed to provide localization. We present a first study on the fusion of pedestrian dead reckoning (PDR) from inertial sensors with position estimates from a WiFi localization system. We outline a possible design of particle filter and analyze its behavior on experimental data. We conclude that the outlined method can help to improve WiFi localization and is especially useful within crowd-sourcing environments.
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