Zhang Chenbin, Qin Ningning, Xue Yanbo, Yang Le
Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China.
Career Science Lab, BOSS Zhipin, Beijing 10028, China.
Sensors (Basel). 2020 Feb 15;20(4):1067. doi: 10.3390/s20041067.
Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted extensive attentions because Wi-Fi access points (APs) are widely deployed and we can obtain the Wi-Fi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classification-based method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved K-Means clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) with the one-versus-one strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in real-world environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.
在过去十年中，室内定位的商业利益一直在增加。许多应用的成功至少部分依赖于室内定位，而室内定位有望提供可靠的室内位置信息。基于Wi-Fi接收信号强度（RSS）的室内定位技术受到了广泛关注，因为Wi-Fi接入点（AP）被广泛部署，并且我们无需额外的硬件成本就能获得Wi-Fi RSS测量值。在本文中，我们提出了一种基于分层分类的方法，作为解决室内定位问题的新方案。在开发的方法中，我们首先采用改进的K均值聚类算法将感兴趣的区域划分为几个区域，并且允许它们相互重叠，以提高后续室内定位过程的泛化能力。为了找到定位结果，采用了K近邻（KNN）算法和采用一对一策略的支持向量机（SVM）。所提出的方法在平板电脑上实现，并在实际环境中评估其性能。实验结果表明，与几种基准方法相比，所提出的方法在位置分类准确率方面提高了1.4%至3.2%，在平均定位误差方面降低了10%至22%。
