Towards Robust Methods for Indoor Localization using Interval Data

Indoor localization has gained an increase in interest recently because of the wide range of services it may provide by using data from the Internet of Things. Notwithstanding the large variety of techniques available, indoor localization methods usually show insufficient accuracy and robustness per...

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Bibliographic Details
Published in2019 20th IEEE International Conference on Mobile Data Management (MDM) pp. 403 - 408
Main Authors Ramdani, Nacim, Zeinalipour-Yazti, Demetrios, Karamousadakis, Michalis, Panayides, Andreas
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2019
Subjects
Data models
Indoor
Interval Analysis
Location
Measurement uncertainty
Modeling
Position measurement
Probability distribution
Radio transmitters
Robustness
Uncertainty
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Summary:Indoor localization has gained an increase in interest recently because of the wide range of services it may provide by using data from the Internet of Things. Notwithstanding the large variety of techniques available, indoor localization methods usually show insufficient accuracy and robustness performance because of the noisy nature of the raw data used. In this paper, we investigate ways to work explicitly with range of data, i.e., interval data, instead of point data in the localization algorithms, thus providing a set-theoretic method that needs no probabilistic assumption. We will review state-of-the-art infrastructure-based localization methods that work with interval data. Then, we will show how to extend the existing infrastructure-less localization techniques to allow explicit computation with interval data. The preliminary evaluation of our new method shows that it provides smoother and more consistent localization estimates than state-of-the-art methods.
ISSN:2375-0324
DOI:10.1109/MDM.2019.00-12