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    牛建伟 刘 洋 卢邦辉 宋文芳. 一种基于Wi-Fi 信号指纹的楼宇内定位算法[J]. 计算机研究与发展, 2013, 50(3): 568-577.
    引用本文: 牛建伟 刘 洋 卢邦辉 宋文芳. 一种基于Wi-Fi 信号指纹的楼宇内定位算法[J]. 计算机研究与发展, 2013, 50(3): 568-577.
    Niu Jianwei, Liu Yang, Lu Banghui, and Song Wenfang. An In-Building Localization Algorithm Based on Wi-Fi Signal Fingerprint[J]. Journal of Computer Research and Development, 2013, 50(3): 568-577.
    Citation: Niu Jianwei, Liu Yang, Lu Banghui, and Song Wenfang. An In-Building Localization Algorithm Based on Wi-Fi Signal Fingerprint[J]. Journal of Computer Research and Development, 2013, 50(3): 568-577.

    一种基于Wi-Fi 信号指纹的楼宇内定位算法

    An In-Building Localization Algorithm Based on Wi-Fi Signal Fingerprint

    • 摘要: 由于GPS无法在楼宇内使用,而目前的楼宇内定位技术一般都需要预先部署额外的设施,因此楼宇内无基础设施定位成为了一个热点研究问题.提出了一种利用Wi-Fi接入点的MAC地址和RSSI(received signal strength indication)值,通过机器分类的方式实现楼宇内房间级定位的算法R-kNN(relativity k-nearest neighbor).R-kNN是一种属性加权k近邻算法,它通过将AP之间的相关性反应在权值的分配上,有效地降低了维度冗余对分类准确率的负面影响.R-kNN没有对房间和AP的物理位置做出任何假设,只需要使用环境中现存的AP就可以取得较好的定位效果,无需部署任何额外设施或修改现有设施.实验结果表明,在AP数量较多的楼宇环境中,R-kNN能够取得比k近邻算法和朴素贝叶斯分类器更好的定位效果.

       

      Abstract: Since GPS cannot be used under in-building environment and current in-building localization approaches require pre-installed infrastructure, in-building localization becomes a problem demanding prompt solutions for location-based services. Therefore, this paper proposes a novel room-level in-building localization algorithm R-kNN (relativity k-nearest neighbor), which solves the localization problem by leveraging MAC address and RSSI (received signal strength indication) of Wi-Fi access points (APs) deployed in buildings. R-kNN falls into category of property-weighted k-nearest neighbor algorithm. By assigning the weight of each AP according to the relativity between AP pairs, R-kNN can reduce the negative effect of dimension redundancy. Moreover, since it makes no assumption on the physical distribution of rooms and APs, R-kNN can work well with existing APs without deploying any new infrastructure or modifying the existing ones. Experimental results demonstrate that when a large number of APs are available, the localization accuracy of R-kNN is bigger than those of the original kNN algorithm and nave Bayes classifier, while its false positive ratio and false negative ratio is smaller than those of the original kNN algorithm and Nave Bayes classifier in most cases.

       

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