Abstract
As industrial and academic communities become increasingly interested in Indoor Positioning Systems (IPSs), a plethora of technologies are gaining maturity and competing for adoption in the global smartphone market. In the near future, we expect busy places, such as schools, airports, hospitals and large businesses, to be outfitted with multiple IPS infrastructures, which need to coexist, collaborate and / or compete for users. In this paper, we examine the novel problem of estimating the accuracy of co-located positioning systems, and selecting which one to use where. This is challenging because 1) we do not possess knowledge of the ground truth, which makes it difficult to empirically estimate the accuracy of an indoor positioning system; and 2) the accuracy reported by a positioning system is not always a faithful representation of the real accuracy. In order to address these challenges, we model the process of a user moving in an indoor environment as a Hidden Markov Model (HMM), and augment the model to take into account vector (instead of scalar) observations, and prior knowledge about user mobility drawn from personal electronic calendars. We then propose an extension of the Baum-Welch algorithm to learn the parameters of the augmented HMM. The proposed HMM-based approach to learning the accuracy of indoor positioning systems is validated and tested against competing approaches in several real-world indoor settings.
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Wen, H., Xiao, Z., Trigoni, N., Blunsom, P. (2013). On Assessing the Accuracy of Positioning Systems in Indoor Environments. In: Demeester, P., Moerman, I., Terzis, A. (eds) Wireless Sensor Networks. EWSN 2013. Lecture Notes in Computer Science, vol 7772. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36672-7_1
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DOI: https://doi.org/10.1007/978-3-642-36672-7_1
Publisher Name: Springer, Berlin, Heidelberg
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