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.
Highlights
The recurrent neural networks can be applied to extract the features of normalized received signal strength indications (RSSIs) of the j-th base station from a cellular network at time ti is defined as c j,i, in time series data, so this study considers and analyzes the normalized RSSIs with multiple consecutive and rneural in accordance the “Recurrent minimum value maximum the RSSIs presents
The lower location errors can be obtained by the recurrent neural networks with multiple consecutive 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
Wi-Fi-based positioning methods can precisely estimate the locations of mobile stations, but the transmission coverage of Wi-Fi access points (APs) is not enough in outdoor environments
Summary
With the development of wireless networks and mobile networks, the techniques of location-based services (LBS) can provide the corresponding services to the users according to users’ current locations. For LBS in outdoor environments, global positioning system (GPS) and assisted GPS (A-GPS) are popular techniques and meet most of the positioning requirements. These techniques may no longer be applicable if the problems of multi-path propagation of wireless signals exist [13]. Some studies proposed cellular-based positioning methods to analyze the signals of cellular networks for reliably estimating the locations of mobile stations [1,5,9,10]
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