An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Positioning

被引：57
|
作者：
Li, Changgeng [1 ]
Qiu, Zhengyang [1 ]
Liu, Changtong [1 ]
机构：
[1] Cent S Univ, Sch Phys & Elect, Middle Xiaoxiang Rd, Changsha, Hunan, Peoples R China
来源：
WIRELESS PERSONAL COMMUNICATIONS | 2017年 / 96卷 / 02期
关键词：
Manhattan distance; Euclidean distance; Indoor positioning; Wi-Fi; WKNN; TOA; LOCALIZATION; LOCATION; SYSTEMS;
D O I：
10.1007/s11277-017-4295-z
中图分类号：
TN [电子技术、通信技术];
学科分类号：
0809 ;
摘要：
The weighted K-nearest neighbor algorithm (WKNN) is widely used in indoor positioning based on Wi-Fi. However, the accuracy of this traditional algorithm using Euclidean distance is not high enough due to the ignorance of statistical regularities from the training set. In this paper, the Manhattan distance is introduced to the WKNN algorithm to distinguish the influence of different reference nodes. Simultaneously, a new method is proposed to increase the accuracy of the algorithm by adjusting the weight of adjacent reference nodes. The simulation and experiment results show that the improved algorithm can have a better performance by increasing the accuracy by 33.82%.
引用
收藏
页码：2239 / 2251
页数：13
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