Unsupervised labelling of sequential data for location identification in indoor environments
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 for...
| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Elsevier
2016
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/33787/ |