Improving indoor geomagnetic field fingerprinting using recurrence plot-based convolutional neural networks

Geomagnetic field fingerprinting is gradually substituting Bluetooth and WiFi fingerprinting since the magnetic field is ubiquitous and independent of any infrastructure. Many studies have used Convolutional Neural Networks (CNNs) to develop indoor positioning systems. Most of these networks use actual magnetic values to build fingerprints. The main source of diminished accuracy is that these CNNs cannot solve the distribution issue of the same magnetic field values. To remedy this limitation, there is a recent interest in applying CNNs to sequences of actual and past data, but no comparative studies have shown the performance contribution of this alternative. In this paper, we propose a CNN-based magnetic fingerprinting system using Recurrence Plots (RPs) as sequence fingerprints. To fairly compare the proposed system with an existing solution treating instantaneous magnetic data, the same real-world data in an indoor environment are used. Testing results show location classification accuracies of 94.92% and 95.46% for the cases of using one RP and three RPs, respectively. As for the localisation error, results show that sequence pattern recognition results in at least a seven-fold decrease in mean distance error.