Unsupervised labelling of sequential data for location identification in indoor environments
作者：
Highlights：
• Presents indoor positioning as an unsupervised labelling task on sequential data.
• Forms a spatial classifier without resorting to pre-determined maps.
• Differentiates location between unknown closely spaced zones indoors.
• Presents a valuable working framework for real-world positioning problems.
• Extends literature studying applications of graphical models.
摘要
•Presents indoor positioning as an unsupervised labelling task on sequential data.•Forms a spatial classifier without resorting to pre-determined maps.•Differentiates location between unknown closely spaced zones indoors.•Presents a valuable working framework for real-world positioning problems.•Extends literature studying applications of graphical models.
论文关键词：Unsupervised labelling,Sequential data,Indoor positioning,Ubiquitous computing,Graphical models
论文评审过程：Received 22 February 2016, Revised 24 April 2016, Accepted 2 June 2016, Available online 3 June 2016, Version of Record 14 June 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.06.003
