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Electronics 2019, 8(1), 59; https://doi.org/10.3390/electronics8010059

A Mobile Positioning Method Based on Deep Learning Techniques

1
School of Economics and Management, Fuzhou University, Fuzhou 350116, China
2
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
*
Author to whom correspondence should be addressed.
Received: 10 October 2018 / Revised: 17 December 2018 / Accepted: 28 December 2018 / Published: 4 January 2019
(This article belongs to the Section Networks)
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Abstract

This 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
Keywords: deep learning; recurrent neural networks; mobile positioning method; fingerprinting positioning method; received signal strength deep learning; recurrent neural networks; mobile positioning method; fingerprinting positioning method; received signal strength
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wu, L.; Chen, C.-H.; Zhang, Q. A Mobile Positioning Method Based on Deep Learning Techniques. Electronics 2019, 8, 59.

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