Indoor Positioning Based on Fingerprint-Image and Deep Learning

Wi-Fi and magnetic field fingerprinting have been a hot topic in indoor positioning researches because of their ubiquity and location-related features. Wi-Fi signals can provide rough initial positions, and magnetic fields can further improve the positioning accuracies, therefore many researchers have tried to combine the two signals for high-accuracy indoor localization. Currently, state-of-the-art solutions design separate algorithms to process different indoor signals. Outputs of these algorithms are generally used as inputs of data fusion strategies. These methods rely on computationally expensive particle filters, labor-intensive feature analysis, and time-consuming parameter tuning to achieve better accuracies. Besides, particle filters need to estimate the moving directions of particles, limiting smartphone orientation to be stable, and aligned with the user's moving directions. In this paper, we adopted a convolutional neural network (CNN) to implement an accurate and orientation-free positioning system. Inspired by the state-of-the-art image classification methods, we design a novel hybrid location image using Wi-Fi and magnetic field fingerprints, and then a CNN is employed to classify the locations of the fingerprint images. In order to prevent the overfitting problem of the positioning CNN on limited training datasets, we also propose to divide the learning process into two steps to adopt proper learning strategies for different network branches. We show that the CNN solution is able to automatically learn location patterns, thus significantly lower the workforce burden of designing a localization system. Our experimental results convincingly reveal that the proposed positioning method achieves an accuracy of about 1 m under different smartphone orientations, users, and use patterns.