Abstract
This study proposes a 2D surface correlation-based indoor localization technology using LTE fingerprinting with an accuracy of several meters. The most important problem with RF fingerprinting is that the location discernment of signal strength becomes exceedingly low as the distance from the RF signal source increases. Instantaneous RSS measurement based conventional fingerprinting involves the installation of several signal sources to improve location discernment. However, additional installations of LTE base stations (BSs) are impossible. To improve location discernment, the proposed technology utilizes a spatial RSS pattern extracted using the Pedestrian-Dead Reckoning during user movement. The use of the proposed technology greatly improves the accuracy and availability of LTE signals using the pattern. Additionally, the following two points should be considered. First, the spatially accumulated pattern contains location errors that can cause pattern distortion. The proposed technology performs pattern correction through feature matching using RSS mark and crossroad locations. Second, the accuracy of pattern matching may be decreased prior to sufficient pattern accumulation. For the rapid convergence of the pattern matching, the proposed technology performs correlation pattern analysis. This approach detects the point in which the discernment is increased by pattern accumulation and limits the search range around the matching point. To verify the performance, we conducted tests in a shopping mall where only one LTE BS ID is available. Consequently, the convergence distance of pattern matching was improved by 69% after pattern analysis. Furthermore, it was confirmed that the localization error after convergence improved from 4.16 m to 2.82 m.
Highlights
Seamless navigation is important for providing location based services (LBS) to users
In this study, we have proposed a 2D surface correlation-based precise localization technology using LTE fingerprinting for indoor localization, which is the core of seamless navigation
The proposed technology improved the location discernment using the surface which presented the spatial RSS pattern generated from the pedestrian dead-reckoning (PDR) during a user’s movement. It prevented pattern distortion caused by the PDR drift error through a feature matching algorithm using feature information such as the RSS mark and crossroad coordinates
Summary
Seamless navigation is important for providing location based services (LBS) to users. We have already proposed a precise localization technology based on surface correlation using LTE signals in urban areas [17] This technology improves the discernment and accuracy of the LTE fingerprinting method in a complex urban area by utilizing 1D spatial RSS patterns accumulated during user movement. We propose a 2D surface correlation based fingerprinting method that utilizes the spatial RSS pattern accumulated in 2D space, rather than comparing the instantaneous RSS measurement directly with a database. The RSS propagation model is applied to the RP on the collection path to define the RSS for the virtual RPs. The conventional fingerprinting method directly compares the instantaneous RSS measurement with the database and estimates the location, so the level of discernment is exceedingly poor in an LTE environment where similar RSS patterns may exist. To estimate the actual heading in comparison with the DB, the proposed technology detects the rotation angle with a minimum similarity value by rotating the surface at a specific angle θ
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