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In this study, a 3D positioning method is proposed for hospital applications, such as navigation within a hospital building. It employs deep learning algorithms to analyze the received signal strength from cellular networks and Wi-Fi access points in order to estimate the positions of mobile stations. A two-stage deep learning procedure (level classification and location determination) is constructed to obtain the exact position information (building level, longitude, and latitude) in multiple-level buildings. To evaluate the performance of the proposed method, an experiment was conducted in the hospital of Xi’an Polytechnic University. In total, 36,985 records, 42 sampling location points, 28 different cellular networks, and 289 different Wi-Fi access points were considered. A deep learning neural network was trained for the first stage of level classification. Three deep learning neural networks were trained to obtain the distinct location coordinates (longitude and latitude) for three different building levels. To compare the efficacy of heterogeneous networks, three kinds of neural networks with different inputs (only cellular, only Wi-Fi APs, and a conjunction of cellular and Wi-Fi APs) were implemented. The accuracy of level classification was shown to be 100% for only Wi-Fi APs as an input. The average distance error of the location determination for different floors was 0.28 m for only Wi-Fi APs and for the conjunction of Wi-Fi APs and cellular networks in the second stage.

A 3D mobile positioning method based on deep learning for hospital applications