[1]席志红,占梦奇.基于位置范围限定的WiFi-KNN室内定位算法[J].应用科技,2020,47(4):66-70.[doi:10.11991/yykj.201912002]
 XI Zhihong,ZHAN Mengqi.WiFi-KNN indoor positioning algorithm based on location range limitation[J].Applied science and technology,2020,47(4):66-70.[doi:10.11991/yykj.201912002]
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基于位置范围限定的WiFi-KNN室内定位算法(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第47卷
期数:
2020年4期
页码:
66-70
栏目:
计算机技术与应用
出版日期:
2020-07-05

文章信息/Info

Title:
WiFi-KNN indoor positioning algorithm based on location range limitation
作者:
席志红 占梦奇
哈尔滨工程大学 信息与通信工程学院,黑龙江 哈尔滨 150001
Author(s):
XI Zhihong ZHAN Mengqi
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
WiFi指纹数据库接收信号强度K-近邻算法RSS直方图范围因子时间波动累积分布函数
Keywords:
WiFi fingerprint databasereceived signal strengthK-nearest neighbor algorithm(KNN)RSS histogramrange factortime fluctuationcumulative distribution function
分类号:
TP301
DOI:
10.11991/yykj.201912002
文献标志码:
A
摘要:
针对传统的基于WiFi的最近邻(K-nearest neighbor algorithm, WiFi-KNN)室内定位算法精确度不能达到精准定位的需求的问题,本文提出了一种基于位置范围限定的K近邻(K-nearest neighbor based on the location range limit , LRL-KNN)室内定位算法。LRL-KNN算法通过利用用户的先前位置与WiFi指纹数据库中的参考点位置之间的物理距离组成的相关范围因子来缩放指纹距离,以此来减少定位的空间歧义性。尽管利用了先前的位置,但是该算法并不需要知道用户的确切移动速度和方向。与此同时,考虑到WiFi接收信号强度的时间波动性,将RSS直方图合并到距离计算中来减小时间波动带来的影响。实验结果表明:传统KNN算法的平均定位误差为2.13 m,新算法的平均定位误差为1.80 m,该误差在相同的测试环境下比传统的KNN算法减少15%。
Abstract:
In order to solve the problem that the accuracy of traditional WiFi-KNN indoor location algorithm cannot meet the requirements of accurate location, a K-nearest neighbor based on the location range limit (LRL-KNN) indoor positioning algorithm is proposed in this paper. The traditional KNN algorithm calculates the matching distance between the user’s fingerprint at this location and the fingerprint at the reference point in the database, and then sorts the nearest neighbor reference points by the fingerprint distance. However, LRL-KNN algorithm uses a range factor composed of the physical distance between the user’s previous position and the reference point position in the WiFi fingerprint database to scale the fingerprint distance, so as to reduce the spatial ambiguity of location. Although using the previous position, the LRL-KNN algorithm does not need to know the exact moving speed and direction of the user. Meanwhile, considering the time fluctuation of the received signal strength of WiFi, the RSS histogram is incorporated into the distance calculation to reduce the impact of time fluctuation. The experimental results show that the average positioning error of the traditional KNN algorithm is 2.13 m, and that of the new algorithm is 1.80 m, and the former is 15% higher than the latter in the same test environment.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-12-02。
作者简介:席志红,女,教授,博士生导师;占梦奇,男,硕士研究生
通讯作者:占梦奇,E-mail:17745164032@163.com
更新日期/Last Update: 2020-11-27