Smart Systems Engineering Lab, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia.
Communications Research Center, School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel). 2022 Jul 13;22(14):5236. doi: 10.3390/s22145236.
Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. Indoor positioning system (IPS) research has attained robust location-based services in complex indoor structures. Unforeseeable propagation loss in complex indoor environments results in poor localization accuracy of the system. Various IPSs have been developed based on fingerprinting to precisely locate an object even in the presence of indoor artifacts such as multipath and unpredictable radio propagation losses. However, such methods are deleteriously affected by the vulnerability of fingerprint matching frameworks. In this paper, we propose a novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data. BoF calculates the vocabulary set using k-mean clustering, where the frequency of the vocabulary in the raw fingerprint data represents the robust final features that improve localization accuracy. Experimental results from simulation-based indoor scenarios and real-time experiments demonstrate that the proposed framework outperforms previously developed models.
基于位置的服务已经渗透到智能学术机构中，提高了高等教育的质量。人和物体的位置信息可以预测不同的潜在需求，并提供相关服务来满足这些需求。室内定位系统（IPS）的研究在复杂的室内结构中实现了强大的基于位置的服务。复杂室内环境中不可预测的传播损耗导致系统定位精度差。已经开发了各种基于指纹识别的 IPS，即使在存在室内干扰（如多径和不可预测的无线电传播损耗）的情况下，也可以精确地定位物体。然而，这种方法受到指纹匹配框架的脆弱性的不利影响。在本文中，我们提出了一个新的机器学习框架，包括特征袋和随后的 k-最近邻分类器，将最终特征分类为其各自的地理坐标数据。BoF 使用 k-均值聚类计算词汇集，其中词汇在原始指纹数据中的频率表示提高定位精度的稳健最终特征。基于仿真的室内场景和实时实验的实验结果表明，所提出的框架优于以前开发的模型。
