[1]尹铁源,王智晶.基于 KNN-RF 的 AP 点补偿方法研究[J].计算机技术与发展,2022,32(S1):66-69.[doi:10. 3969 / j. issn. 1673-629X. 2022. S1. 015]
　YIN Tie-yuan,WANG Zhi-jing.Research on AP Point Compensation Method Based on KNN-RF[J].,2022,32(S1):66-69.[doi:10. 3969 / j. issn. 1673-629X. 2022. S1. 015]
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基于 KNN-RF 的 AP 点补偿方法研究()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年S1期
页码:
66-69
栏目:
应用前沿与综合
出版日期:
2022-12-11

文章信息/Info

Title:
Research on AP Point Compensation Method Based on KNN-RF
文章编号:
1673-629X(2022)S1-0066-04
作者:
尹铁源; 王智晶
沈阳工业大学 信息科学与工程学院,辽宁 沈阳 110870
Author(s):
YIN Tie-yuan; WANG Zhi-jing
School of Information Science and Engineering,Shenyang University of Technology,Shenyang 110870,China
关键词:
室内定位; WiFi; 随机森林; 缺失值; 信号补偿
Keywords:
indoor positioning; WiFi; random forest; missing value; signal compensation
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2022. S1. 015
摘要:
随着智能设施的普及,以及人们室内活动的增多,一款能够在室内环境下提供高精度的定位技术成为当今世界最为迫切的需求之一。 在众多的室内定位技术中,基于 WiFi 的室内定位技术由于成本低廉、精度高等特点受到了广泛的青睐。 在基于 WiFi 的室内定位技术中,由于室内环境复杂和走动人员的随机性等,导致 WiFi 信号不稳定,各个参考点所采集的数据不完整,缺失率不一。 而实时定位的精度高低与在离线阶段所建立的位置指纹库的质量有着密不可分的关系。因此,提出了一种基于 KNN-RF 的信号补偿方法,该方法针对了不同缺失率情况下的位置指纹库,通过实验表明使用了基于 KNN-RF 的信号补偿方法后,在线的定位精度有了明显的提升。
Abstract:
With the popularity of intelligent facilities and the increase of people ’ s indoor activities, a high - precision positioningtechnology in the indoor environment has become one of the most urgent needs in the world. In the indoor positioning technology basedon WiFi,due to the complex indoor environment and the randomness of walking people,the WiFi signal is unstable,the data collected byeach reference point is incomplete,and the missing rate is different. The accuracy of real-time positioning is closely related to the qualityof the fingerprint database established in the offline stage. Therefore,we propose a signal compensation scheme based on KNN - RF,aiming at the location fingerprint database with different missing rate. The experiment shows that the online positioning accuracy has beensignificantly improved after using the signal compensation scheme based on KNN-RF.

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更新日期/Last Update: 2022-06-10