• 网络与通信 •

### 基于改进AP选择和K最近邻法算法的室内定位技术

1. 1. 辽宁工程技术大学 电子与信息工程学院, 辽宁 葫芦岛 125105;
2. 辽宁工程技术大学 研究生院, 辽宁 葫芦岛 125105
• 收稿日期:2017-05-04 修回日期:2017-06-27 出版日期:2017-11-10 发布日期:2017-11-11
• 通讯作者: 侯跃
• 作者简介:李新春(1963-),男,辽宁朝阳人,高级工程师,主要研究方向:无线传感器网络、嵌入式系统、数字图像处理;侯跃(1992-),女,河北唐山人,硕士研究生,主要研究方向:无线传感器网络。

### Indoor positioning technology based on improved access point selection and K nearest neighbor algorithm

1. 1. School of Electrics and Information Engineering, Liaoning Technical University, Huludao Liaoning 125105, China;
2. Graduate School, Liaoning Technical University, Huludao Liaoning 125105, China
• Received:2017-05-04 Revised:2017-06-27 Online:2017-11-10 Published:2017-11-11

Abstract: Since indoor environment is complex and equal signal differences are assumed to equal physical distances in the traditional K Nearest Neighbor (KNN) approach, a new Access Point (AP) selection method and KNN indoor positioning algorithm based on scaling weight were proposed. Firstly, in the improved AP selection method, box plot was used to filter Received Signal Strength (RSS) outliers and create a fingerprint database. The AP with high loss rate in the fingerprint database were removed. The standard deviation was used to analyze the variations of RSS, and TOP-N APs with less interference were selected. Secondly, the scaling weight was introduced into the traditional KNN algorithm to construct a scaling weight model based on RSS. Finally, the first K reference points which obtained the minimum effective signal distance were calculated to get the unknown position coordinates. In the localization simulation experiments, the mean of error distance by improved AP selection method is 21.9% lower than that by KNN. The mean of error distance by the algorithm which introduced scaling weight is 1.82 m, which is 53.6% lower than that by KNN.