Abstract
We consider the problem of real-time sensing and tracking the location of a moving cart in an indoor environment. To this end, we propose to combine position information obtained from an inertial navigation system (INS) and a short-range wireless reference system that can be embedded into a future“network of things”. The data produced by the INS lead to accurate short-term position estimates, but due to the drifts inherent to the system, these estimates perform loosely after some time. To solve this problem, we also generate estimates with a wireless reference system. These radio-based estimates can be used as accurate long-term position estimates because their accuracy improves over time as the channel fading can be averaged out. We use a data fusion algorithm based on Kalman filtering with forward/backward smoothing to optimally combine the short- and long-term position estimates. We have implemented this localization system in a real-time testbed. The measurement results, which we obtained using the proposed method, show considerable improvements in accuracy of the location estimates.
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© 2008 Springer-Verlag Berlin Heidelberg
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Coronel, P., Furrer, S., Schott, W., Weiss, B. (2008). Indoor Location Tracking Using Inertial Navigation Sensors and Radio Beacons. In: Floerkemeier, C., Langheinrich, M., Fleisch, E., Mattern, F., Sarma, S.E. (eds) The Internet of Things. Lecture Notes in Computer Science, vol 4952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78731-0_21
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DOI: https://doi.org/10.1007/978-3-540-78731-0_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78730-3
Online ISBN: 978-3-540-78731-0
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