Localization has attracted considerable attention in recent years because of the growth in location‐based services and computer‐aided technology. In particular, wireless fingerprint‐based positioning techniques have received considerable attention due to their satisfactory positioning performance with smartphones. In this work, a two‐level hierarchical structure positioning system is proposed to achieve satisfactory positioning accuracy. First, long‐time‐evolution (LTE) mobile networks are used to provide positioning information, and the collected measurements of LTE signals are converted into grayscale fingerprint images to construct the fingerprint database. To overcome the instability of LTE signals, several data enhancement methods are leveraged to increase the diversity of the fingerprint database. Second, a modified deep residual network (MResNet) coarse localizer is used to learn reliable features from the fingerprint image database. Then, inspired by the ideas of feature fusion and transfer learning, a multilayer perceptron (MLP)‐based fine localizer is used to further learn the features of the fingerprint images and achieve a better positioning performance. Our experimental results convincingly reveal that the proposed positioning system can achieve satisfactory positioning performance in a variety of outdoor environments.
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Outdoor positioning based on deep learning and wireless network fingerprint technology
Semantic Scholar · Computer Science · 2020
Abstract
Localization has attracted considerable attention in recent years because of the growth in location‐based services and computer‐aided technology. In particular, wireless fingerprint‐based positioning techniques have received considerable attention due to their satisfactory positioning performance with smartphones. In this work, a two‐level hierarchical structure positioning system is proposed to achieve satisfactory positioning accuracy. First, long‐time‐evolution (LTE) mobile networks are used to provide positioning information, and the collected measurements of LTE signals are converted into grayscale fingerprint images to construct the fingerprint database. To overcome the instability of LTE signals, several data enhancement methods are leveraged to increase the diversity of the fingerprint database. Second, a modified deep residual network (MResNet) coarse localizer is used to learn reliable features from the fingerprint image database. Then, inspired by the ideas of feature fusion and transfer learning, a multilayer perceptron (MLP)‐based fine localizer is used to further learn the features of the fingerprint images and achieve a better positioning performance. Our experimental results convincingly reveal that the proposed positioning system can achieve satisfactory positioning performance in a variety of outdoor environments.