基于特征匹配和距离加权的蓝牙定位算法

doi:10.11772/j.issn.1001-9081.2018020295

计算机应用 ›› 2018, Vol. 38 ›› Issue (8): 2359-2364.DOI: 10.11772/j.issn.1001-9081.2018020295

基于特征匹配和距离加权的蓝牙定位算法

陆明炽, 王守华, 李云柯, 纪元法, 孙希延, 邓桂辉

卫星导航与位置感知重点实验室(桂林电子科技大学), 广西桂林 541004

收稿日期:2018-02-01 修回日期:2018-03-29 出版日期:2018-08-10 发布日期:2018-08-11
通讯作者: 王守华
作者简介:陆明炽(1990-),男,广西桂平人,硕士研究生,主要研究方向:室内导航、深度学习;王守华(1975-),男,山东滨州人,副教授,硕士,主要研究方向:信息处理、导航定位;李云柯(1993-),男,云南昆明人,硕士研究生,主要研究方向:高精度定位、形变监测;纪元法(1975-),男,山东聊城人,教授,博士,主要研究方向:卫星通信、卫星导航、数字信号处理;孙希延(1973-),女,山东潍坊人,研究员,博士,主要研究方向:卫星导航、电子对抗;邓桂辉(1989-),男,广东茂名人,硕士研究生,主要研究方向:组合导航、室内导航。
基金资助:
广西科技重大专项（桂科AA17202033）；桂林电子科技大学研究生教育创新计划项目（2018YJCX28）。

Bluetooth location algorithm based on feature matching and distance weighting

LU Mingchi, WANG Shouhua, LI Yunke, JI Yuanfa, SUN Xiyan, DENG Guihui

Key Laboratory of Satellite Navigation and Location Awareness(Guilin University of Electronic Technology), Guilin Guangxi 541004, China

Received:2018-02-01 Revised:2018-03-29 Online:2018-08-10 Published:2018-08-11
Supported by:
This work is partially supported by the Major Project of Science and Technology of Guangxi (桂科AA17202033), the Postgraduate Innovation Education Project of Guilin University of Electronic Technology (2018YJCX28).

摘要/Abstract

摘要： 针对传统iBeacon指纹定位技术中接收信号强度值（RSSI）波动较大、指纹库聚类复杂、存在较大跳变性定位误差等问题，提出一种基于排序特征匹配和距离加权的蓝牙定位算法。在离线阶段，该算法先对RSSI进行加权滑动窗处理，然后根据RSSI向量大小生成排序特征码等值，并与位置坐标等信息组成指纹信息，形成指纹库；在在线定位阶段，根据排序特征向量指纹匹配定位算法和基于距离的最优加权K最邻近法（WKNN）实现室内行人定位。在定位仿真实验中，该算法可以自动根据特征码进行聚类，从而降低了聚类的复杂度，能实现最大误差在0.952 m内的室内行人定位精度。

关键词: iBeaon信标, 聚类分析, 特征匹配, 距离加权, 行人定位

Abstract: Focusing on the issues that large fluctuation of Received Signal Strength Indication (RSSI), complex clustering of fingerprint database and large positioning error in traditional iBeacon fingerprinting, a new Bluetooth localization algorithm based on sort feature matching and distance weighting was proposed. In the off-line stage, the RSSI vector size was used to generate the sorting characteristic code. The generated code combined with the information of the position coordinates constituted the fingerprint information, to form the fingerprint library. While in the online positioning stage, the RSSI was firstly weighted by sliding window. Then, indoor pedestrian positioning was achieved by using the sort eigenvector fingerprint matching positioning algorithm and distance-based optimal Weighted K Nearest Neighbors (WKNN). In the localization simulation experiments, the feature codes were used for automatical clustering to reduce the complexity of clustering with maximum error of 0.952 m of indoor pedestrian localization.

Key words: iBeacon beacon, cluster analysis, feature matching, distance weighting, pedestrian location

中图分类号:

TN965.72

陆明炽, 王守华, 李云柯, 纪元法, 孙希延, 邓桂辉. 基于特征匹配和距离加权的蓝牙定位算法[J]. 计算机应用, 2018, 38(8): 2359-2364.

LU Mingchi, WANG Shouhua, LI Yunke, JI Yuanfa, SUN Xiyan, DENG Guihui. Bluetooth location algorithm based on feature matching and distance weighting[J]. Journal of Computer Applications, 2018, 38(8): 2359-2364.

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基于特征匹配和距离加权的蓝牙定位算法

Bluetooth location algorithm based on feature matching and distance weighting

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