Unsupervised labelling of sequential data for location identification in indoor environments

Pérez López, Iker, Pinchin, James, Brown, Michael, Blum, Jesse and Sharples, Sarah (2016) Unsupervised labelling of sequential data for location identification in indoor environments. Expert Systems with Applications . ISSN 0957-4174

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In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we form an informative spatial classifier without resorting to a pre-determined map, and show the potential of the approach using both simulated and real data sets. Results demonstrate the ability of the procedure to segregate structures of radio signal observations and form clustered regions in association to areas of interest to the user; thus, we show it is possible to differentiate location between closely spaced zones of variable size and shape.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/796821
Keywords: Unsupervised Labelling; Sequential Data; Indoor Positioning; Ubiquitous Computing; Graphical Models
Schools/Departments: University of Nottingham, UK > Faculty of Engineering
University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1016/j.eswa.2016.06.003
Depositing User: Perez, Iker
Date Deposited: 06 Jun 2016 12:40
Last Modified: 04 May 2020 17:57
URI: https://eprints.nottingham.ac.uk/id/eprint/33787

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