Deep Neural Networks for Indoor Geomagnetic Field Fingerprinting with Regression Approach

Geomagnetic field fingerprinting is an attractive alternative to WiFi and Bluetooth fingerprinting since the magnetic field is omnipresent and independent of any infrastructure. Recently, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been extensively used to provide fingerprinting solutions for indoor positioning based on magnetic data. Yet, no fairly comparative study has been conducted to determine which type of networks is more likely to recognize magnetic sequence patterns. In this study, we propose a CNN using Recurrence Plots (RPs) as sequence fingerprints as well as advanced RNNs treating magnetic sequences as fingerprints. We used the same real-world data in an indoor environment to build and fairly evaluate the proposed systems. Our findings show that RNNs clearly outperform the RP-based CNN, yet results in higher prediction latencies. Overall, promising positioning performances and smooth trajectory estimates are achieved for pedestrian path tracking due to approaching the indoor localization problem from a regression perspective.