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Data Filtering Method for the Optimization of Mobile Positioning Based on Deep Learning

In this paper, a data filtering method is proposed to filter out the interference of mobile network signals and location errors from global positioning system in training data for the improvement of mobile positioning. Furthermore, a mobile positioning based on deep learning is proposed to analyze the received signal strength indications (RSSIs) from cellular networks and Wi-Fi networks for determining the location of mobile station. In practical experimental environments, a case study of Fujian University of Technology is selected to evaluate the proposed methods. This study collected 3655 records which include 77 different base stations, and 1947 different Wi-Fi access points. The experimental results showed that the average location errors of the proposed method was 4.32m.