Comparing CNN and LSTM Networks for Magnetic Localization of IoT Devices and Pedestrian Tracking

When outdoors, nowadays it is common to rely on Global Navigation Satellite Systems (GNSS) based navigation to find a location. However, for indoor environments no common solution exists, as GNSS positioning is not available indoors. While many substitute technologies rely on infrastructure installed in buildings, e.g., beacons, in this paper we use the magnetic field characteristics of buildings as a solution that is available everywhere. In the implementation, a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) model are used for classification of the characteristics and regression, respectively. The approaches are evaluated against each other on a public dataset showing that the magnetic field can be a robust ubiquitous solution for indoor localization. The regression with LSTM shows the highest precision, while the error of a classification approach is constrained by the building boundaries and enables the usage of class confidence values for further processing.