Abstract
In order for indoor positioning services to be able to assert themselves across a broad front, simple processes with minimal costs and low user resistance are preferable. This is where our contribution kicks in: We present a method to detect individual positions within buildings by the use of locally induced distortions of the earth’s magnetic field with a smartphone alone, without any additional technology to locate the 2D position. To compensate for the lack of exact 2D coordinates, we fuse the sensor data into a gravitational magnetic vector (GMV) and perform a temporal classification based on a recurrent network. In this work we investigate the applicability of Long-Short-Term-Memory (LSTM) networks to find cross-correlations over a time frame. The trained models are available in a smartphone application. With this application the recognition rates of the locations are analyzed.
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Platte, B., Thomanek, R., Roschke, C., Rolletschke, T., Zimmer, F., Ritter, M. (2020). Simplified Indoor Localization Data Acquisition by Use of Recurrent LSTM Networks on Sequential Geomagnetic Vectors. In: Ahram, T., Taiar, R., Gremeaux-Bader, V., Aminian, K. (eds) Human Interaction, Emerging Technologies and Future Applications II. IHIET 2020. Advances in Intelligent Systems and Computing, vol 1152. Springer, Cham. https://doi.org/10.1007/978-3-030-44267-5_17
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DOI: https://doi.org/10.1007/978-3-030-44267-5_17
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