Preprint Article Version 1 This version is not peer-reviewed

A Mobile Positioning Method Based on Deep Learning Techniques

Version 1 : Received: 10 October 2018 / Approved: 11 October 2018 / Online: 11 October 2018 (13:31:28 CEST)

How to cite: Wu, L.; Chen, C.; Zhang, Q. A Mobile Positioning Method Based on Deep Learning Techniques. Preprints 2018, 2018100239 (doi: 10.20944/preprints201810.0239.v1). Wu, L.; Chen, C.; Zhang, Q. A Mobile Positioning Method Based on Deep Learning Techniques. Preprints 2018, 2018100239 (doi: 10.20944/preprints201810.0239.v1).

Abstract

This study proposes a mobile positioning method which 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 statioThis study proposes a mobile positioning method which 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 4,525 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., 2 timestamps and 3 timestamps); the experimental results can be observed that the average error of location estimation was 9.19 meters by the proposed mobile positioning method with 2 timestamps.

Subject Areas

deep learning; recurrent neural networks; mobile positioning method; fingerprinting positioning method; received signal strength

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