A Mobile Positioning Method Based on Deep Learning Techniques
AbstractThis study proposes a mobile positioning method that adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile stations. The recurrent neural networks with multiple consecutive timestamps can be applied to extract the features of time series data for the improvement of location estimation. In practical experimental environments, there are 4525 records, 59 different base stations, and 582 different Wi-Fi access points detected in Fuzhou University in China. The lower location errors can be obtained by the recurrent neural networks with multiple consecutive timestamps (e.g., two timestamps and three timestamps); from the experimental results, it can be observed that the average error of location estimation was 9.19 m by the proposed mobile positioning method with two timestamps. View Full-Text
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Wu, L.; Chen, C.-H.; Zhang, Q. A Mobile Positioning Method Based on Deep Learning Techniques. Electronics 2019, 8, 59.
Wu L, Chen C-H, Zhang Q. A Mobile Positioning Method Based on Deep Learning Techniques. Electronics. 2019; 8(1):59.Chicago/Turabian Style
Wu, Ling; Chen, Chi-Hua; Zhang, Qishan. 2019. "A Mobile Positioning Method Based on Deep Learning Techniques." Electronics 8, no. 1: 59.
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