Adaptive K-nearest neighbour algorithm for WiFi fingerprint positioning

被引：50

作者：

Oh, Jongtaek ^{[1
]}

Kim, Jisu ^{[2
]}

机构：

[1] Hansung Univ, Dept Elect Engn, Seoul, South Korea

[2] Hansung Univ, Dept Comp Engn, Seoul, South Korea

来源：

ICT EXPRESS | 2018年 / 4卷 / 02期

关键词：

KNN; Positioning; Fingerprint;

D O I：

10.1016/j.icte.2018.04.004

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

K-nearest neighbour is one of the most widely used algorithms for indoor positioning systems. However, the error for each estimated position notably varies depending on the K value used for the algorithm. Therefore, if K is a fixed value, the estimation error for the positions cannot be further reduced. In this Letter, I propose an algorithm that adapts the K value for each position by analysing the correlation between the K value and the received WiFi signal strength. The proposed algorithm provides an improvement above 30% on the positioning accuracy compared to the algorithm with fixed K value. (C) 2018 The Korean Institute of Communications and Information Sciences (KICS). Publishing Services by Elsevier B.V.

引用

页码：91 / 94

页数：4

共 9 条

[1]

Bahl P., 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064), P775, DOI 10.1109/INFCOM.2000.832252

[2] Statistical learning theory for location fingerprinting in wireless LANs [J].