基于投票机制的室内WiFi指纹定位算法  

Indoor WiFi fingerprinting algorithm based on voting mechanism

作　　者：王开亮 谢亚琴[1] 宦海 周莉莉 Wang Kailiang;Xie Yaqin;Huan Hai;Zhou Lili(School of Electronics&Information Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,China;School of Artificial Intelligence(School of Future Technology),Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学电子与信息工程学院,南京210044 [2]南京信息工程大学人工智能学院(未来技术学院),南京210044

出　　处：《电子测量技术》2023年第12期61-68,共8页Electronic Measurement Technology

基　　金：国家自然科学基金(62001238)项目资助。

摘　　要：针对传统室内WiFi指纹定位算法中单个距离度量的局限性且未考虑到dBm表示与功率之间的关系的问题,提出一种基于投票机制的室内WiFi指纹定位算法。在采集到接收信号强度(RSS)数据后,首先,对RSS数据进行预处理;然后,基于投票机制对每种距离度量选中的近邻点取交集组成公共近邻点,并统计每个公共近邻点出现的频率;最后,通过概率加权得到最终定位结果。实验结果表明,所提出方法的定位精度为1.63 m,与K近邻(KNN)、斯皮尔曼(Spearman)和肯德尔相关系数(KTCC)方法的定位精度相比,平均定位精度分别提升了10%、33%和58%。此外,与MAN2数据集中的最优定位精度1.86 m相比,定位精度提高了12%。Aiming at the limitation of a single distance metric in the traditional indoor WiFi fingerprinting algorithm and the relationship between dBm representation and power is not considered,an indoor Wi-Fi fingerprinting algorithm based on voting mechanism is proposed.After collecting the received signal strength(RSS) data,first,preprocess the RSS data.Then,based on the voting mechanism,the nearest neighbors selected by each distance metric are intersected to form common neighbors,and count each the frequency of common neighbor points.Finally,the final positioning result is obtained by probability weighting.Experimental results show that the proposed method achieves a localization accuracy of 1.63 m,and the average localization accuracy is improved by 10%,33%,and 58%,respectively,compared with the localization accuracy of KNN,Spearman,and KTCC methods.Furthermore,the localization accuracy is improved by 12% compared to the optimal localization accuracy of 1.86 m in the MAN2 dataset.

关 键 词：室内定位 WiFi指纹 距离度量 KNN 投票机制 

分 类 号：TN925[电子电信—通信与信息系统]