Indoor localization accuracy estimation from fingerprint data
Date
2017Author
Nikitin, A.Laoudias, Christos
Chatzimilioudis, Georgios
Karras, P.
Zeinalipour-Yazdi, Constantinos D.
ISBN
978-1-5386-3932-0Publisher
Institute of Electrical and Electronics Engineers Inc.Source
Proceedings - 18th IEEE International Conference on Mobile Data Management, MDM 201718th IEEE International Conference on Mobile Data Management, MDM 2017
Pages
196-205Google Scholar check
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The demand for indoor localization services has led to the development of techniques that create a Fingerprint Map (FM) of sensor signals (e.g., magnetic, Wi-Fi, bluetooth) at designated positions in an indoor space and then use FM as a reference for subsequent localization tasks. With such an approach, it is crucial to assess the quality of the FM before deployment, in a manner disregarding data origin and at any location of interest, so as to provide deployment staff with the information on the quality of localization. Even though FM-based localization algorithms usually provide accuracy estimates during system operation (e.g., visualized as uncertainty circle or ellipse around the user location), they do not provide any information about the expected accuracy before the actual deployment of the localization service. In this paper, we develop a novel frame-work for quality assessment on arbitrary FMs coined ACCES. Our framework comprises a generic interpolation method using Gaussian Processes (GP), upon which a navigability score at any location is derived using the Cramer-Rao Lower Bound (CRLB). Our approach does not rely on the underlying physical model of the fingerprint data. Our extensive experimental study with magnetic FMs, comparing empirical localization accuracy against derived bounds, demonstrates that the navigability score closely matches the accuracy variations users experience. © 2017 IEEE.
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