Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning

• Published in: IEEE access Vol. 7; pp. 33943 - 33956
• Main Authors: Bhattarai, Bimal, Yadav, Rohan Kumar, Gang, Hui-Seon, Pyun, Jae-Young
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bluetooth; Buildings; Deep learning; Deep recurrent neural network (DRNN); Fingerprint recognition; Fingerprinting; Geomagnetic field; Geomagnetism; Landmarks; Logic gates; long short-term memory (LSTM); Machine learning; Magnetic field measurement; Magnetic fields; Magnetic signals; Positioning devices (machinery); Radio signals; Recurrent neural networks; Signal strength; Smart phones; Support vector machines
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2902573

The unstable nature of radio frequency signals and the need for external infrastructure inside buildings have limited the use of positioning techniques, such as Wi-Fi and Bluetooth fingerprinting. Compared to these techniques, the geomagnetic field exhibits stable signal strength in the time domain. However, existing magnetic positioning methods cannot perform well in a wide space because the magnetic signal is not always discernible. In this paper, we introduce deep recurrent neural networks (DRNNs) to build a model that is capable of capturing long-range dependencies in variable-length input sequences. The use of DRNNs is brought from the idea that the spatial/temporal sequence of magnetic field values around a given area will create a unique pattern over time, despite multiple locations having the same magnetic field value. Therefore, we can divide the indoor space into landmarks with magnetic field values and find the position of the user in a particular area inside the building. We present long short-term memory DRNNs for spatial/temporal sequence learning of magnetic patterns and evaluate their positioning performance on our testbed datasets. The experimental results show that our proposed models outperform other traditional positioning approaches with machine learning methods, such as support vector machine and k-nearest neighbors.