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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Published in | 2019 20th IEEE International Conference on Mobile Data Management (MDM) pp. 403 - 408 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
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IEEE
01.06.2019
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Abstract | 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. |
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AbstractList | 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. |
Author | Ramdani, Nacim Karamousadakis, Michalis Zeinalipour-Yazti, Demetrios Panayides, Andreas |
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Snippet | 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.... |
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SubjectTerms | Data models Indoor Interval Analysis Location Measurement uncertainty Modeling Position measurement Probability distribution Radio transmitters Robustness Uncertainty |
Title | Towards Robust Methods for Indoor Localization using Interval Data |
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