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.%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 naive Bayes classifier, while its false positive ratio and false negative ratio is smaller than those of the original kNN algorithm and Naive Bayes classifier in most cases.
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