Precise Indoor Localization: Rapidly-Converging 2D Surface Correlation-Based Fingerprinting Technology Using LTE Signal

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.

Sensor Response-Based Localization Using Spatial Correlation of Nongeotagged Data

“…1. Observation area at an intersection measurements, its accuracy is comparable to those reported in the literature [13]- [16]. However, the reported accuracy cannot be directly compared with those reported previously because the conditions such as the performance metrics and the situations of localization differ among the publications.…”

“…In smartphone-based indoor localization, several localization methods that use spatial correlation have been proposed, and they were mainly based on deep learning, fingerprinting, or their combination. For example, Lee et al [13] proposed a two-dimensional surface correlation-based fingerprinting technology. Their method utilized a spatial RSS pattern extracted using the pedestrian-dead reckoning (PDR) to improve localization accuracy.…”

“…The localization accuracy of the distance-based method is the performance limit of the proposed method and represents ideal characteristics. First, the intersensor distances calculated using (13) were sorted in ascending order, and a proximity graph was determined using (4). Next, all sensor locations were estimated using the method described in Section refsec:SRlocalization based on the determined proximity graph.…”

Evaluation and Comparison of 5G, WiFi, and Fusion With Incomplete Maps for Indoor Localization

“…WiFi and cellular networks are commonly associated with fingerprinting due to the high density of stations in office and residential areas [18], [31]. Fingerprinting can provide high accuracy with a reduced infrastructure investment, but it requires the creation of a radio map with a complex training phase.…”

“…As a result, each point in space is associated with a set of paired values comprising reference point (RP) identifiers and unvarying received power levels. This principle underlies the concept of fingerprinting, these paired values conform a distinctive signature, commonly referred to as a fingerprint, which serves to uniquely identify each point in space [18].…”