Yang, L., Chen, H., Cui, Q., et al. (2015) Proba-bilistic-KNN: A Novel Algorithm for Passive Indoor-Localization Scenario. Vehicular Technology Conference. IEEE, 1-5.

has been cited by the following article:

• TITLE: Robust Local Weighted Regression for Magnetic Map-Based Localization on Smartphone Platform

  AUTHORS: Zhibin Meng, Mei Wang, Enliang Wang, Xiangyu Xu

  KEYWORDS: Indoor Localization, Magnetic, Particle Filter, Robust Local Weighted Regression Algorithm

  JOURNAL NAME: Journal of Computer and Communications, Vol.5 No.3, March 13, 2017

  ABSTRACT: The magnetic information measured on the smartphone platform has a large fluctuation and the research of indoor localization algorithm based on smart-phone platform is less. Indoor localization algorithm on smartphone platform based on particle filter is studied. Robust local weighted regression is used to smooth the original magnetic data in the process of constructing magnetic map. Use moving average filtering model to filter the online magnetic observation data in positioning process. Compare processed online magnetic data with processed magnetic map collected by smartphone platform and the average matching error is 0.3941uT. Average positioning error is 0.229 meter when using processed online and map data.