Volume XLI-B4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 573-577, 2016
https://doi.org/10.5194/isprs-archives-XLI-B4-573-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 573-577, 2016
https://doi.org/10.5194/isprs-archives-XLI-B4-573-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  14 Jun 2016

14 Jun 2016

FLOOR IDENTIFICATION WITH COMMERCIAL SMARTPHONES IN WIFI-BASED INDOOR LOCALIZATION SYSTEM

H. J. Ai2,3, M. Y. Liu1, Y. M. Shi1, and J. Q. Zhao1 H. J. Ai et al.
  • 1The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing of Wuhan University, 430079, NO.129 Luoyu Road, Wuhan,China
  • 2School of Computer Science of Wuhan University, 430072, NO.129 Luoyu Road, Wuhan, China
  • 3State Key Lab of Software Engineering of Wuhan University, 430072, NO.129 Luoyu Road, Wuhan, China

Keywords: Indoor Localization, WiFi, Fingerprint, Sensor, Floor Identification, KNN, Neural Network

Abstract. In this paper, we utilize novel sensors built-in commercial smart devices to propose a schema which can identify floors with high accuracy and efficiency. This schema can be divided into two modules: floor identifying and floor change detection. Floor identifying module starts at initial phase of positioning, and responsible for determining which floor the positioning start. We have estimated two methods to identify initial floor based on K-Nearest Neighbors (KNN) and BP Neural Network, respectively. In order to improve performance of KNN algorithm, we proposed a novel method based on weighting signal strength, which can identify floors robust and quickly. Floor change detection module turns on after entering into continues positioning procedure. In this module, sensors (such as accelerometer and barometer) of smart devices are used to determine whether the user is going up and down stairs or taking an elevator. This method has fused different kinds of sensor data and can adapt various motion pattern of users. We conduct our experiment with mobile client on Android Phone (Nexus 5) at a four-floors building with an open area between the second and third floor. The results demonstrate that our scheme can achieve an accuracy of 99% to identify floor and 97% to detecting floor changes as a whole.