WKNN indoor location clustering algorithm with triangle correction

DOI

10.2991/fmsmt-17.2017.305How to use a DOI?

Keywords

indoor location, location algorithm, triangle correction

Abstract

Aiming at the problem of low precision in the positioning stage of fingerprint location positioning technology based on WiFi, through the comparative study of the nearest neighbor classification algorithm (NN),K nearest neighbor classification algorithm (KNN) and Bias algorithm, and then we present a kind of weighted KNN algorithm of based on triangle correction. On the one hand, the algorithm uses the difference between the AP signal intensity value of the node and the fingerprint node to be taken as the weighting factor, and the positioning accuracy of KNN is improved by the contribution ratio of different fingerprint nodes; On the other hand, we further improve the positioning accuracy by selecting three nearest neighbor points which the unknown node must be in the triangle composed of these three points. Finally, the simulation results showed the effectiveness of the algorithm.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Di Zhou
AU  - Haiyan Lan
AU  - Guoyin Zhang
AU  - Xuesong Ma
AU  - Eryue Liang
PY  - 2017/04
DA  - 2017/04
TI  - WKNN indoor location clustering algorithm with triangle correction
BT  - Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)
PB  - Atlantis Press
SP  - 1570
EP  - 1574
SN  - 2352-5401
UR  - https://doi.org/10.2991/fmsmt-17.2017.305
DO  - 10.2991/fmsmt-17.2017.305
ID  - Zhou2017/04
ER  -