Location estimation in large indoor multi-floor buildings using hybrid networks
Location estimation in large indoor multi-floor buildings using hybrid networks
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DOI：
10.1109/wcnc.2013.6554893
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发表时间：
2013-04
期刊：
影响因子：
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通讯作者：
Kejiong Li;J. Bigham;E. Bodanese;L. Tokarchuk
中科院分区：
文献类型：
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作者：
Kejiong Li;J. Bigham;E. Bodanese;L. Tokarchuk
本文介绍了一种结合WiFi和GSM网络接收到的信号强度（RSS）数据的室内位置估计方法的结果。之前的研究主要集中在相对较小的室内环境。在许多潜在的应用程序中，获得大致的位置信息，例如移动用户在哪个房间，就足够了。采用分层聚类方法对RSS空间进行分区。为了在分区中选择最佳发射机，我们通过将RSS元组转换为主成分（pc）来评估可归因于不同基站（BSs）或接入点（ap）的RSS方差量。这使我们能够在更少的维度中保留可探测发射机的大部分有用信息。在我们的实验中，我们收集了玛丽女王校区电子工程（EE）大楼2楼和3楼的WiFi和蜂窝RSS。实验结果表明，该方法可以提供较好的房间预测精度，特别是当我们将WiFi RSS和GSM RSS结合在一起进行定位时。
This paper presents results for an approach for indoor location estimation that integrates received signal strength (RSS) data from both WiFi and GSM networks. Previous work has focused on relatively small indoor environments. In many potential applications, getting approximate location information, such as in which room the mobile user is, is adequate. A hierarchical clustering method is used to partition the RSS space. To choose the best transmitters in a partition, we assess the amount of RSS variance that is attributable to different base stations (BSs) or access points (APs) by transforming the RSS tuples into principal components (PCs). This allows us to retain most of the useful information of detectable transmitters in fewer dimensions. In our experiments, we collected WiFi and cellular RSS on the 2nd and 3rd-floor electronic engineering (EE) building in Queen Mary campus. The experiment results show that the proposed method can provide a good accuracy of room prediction, especially when we integrate WiFi RSS with GSM RSS together to do the positioning.