This paper proposes and validates, in the field, an approach for position tracking that is based on Long-Term Evolution (LTE) downlink signal measurements. A setup for real data live gathering is used to collect LTE signals while driving a car in the town of Rapperswil, Switzerland. The collected data are then processed to extract the received LTE cell-specific reference signals (CRSs), which are exploited for estimating pseudoranges. More precisely, the pseudoranges are evaluated by using the “ESPRIT and Kalman Filter for Time-of-Arrival Tracking” (EKAT) algorithm and by taking advantage of signal combining in the time, frequency, spatial, and cell ID domains. Finally, the pseudoranges are corrected for base station's clock bias and drift, which are previously estimated, and are used in a positioning filter. The obtained results demonstrate the feasibility of a position tracking system based on the reception of LTE downlink signals

This paper proposes an algorithm for the estimation and tracking of the direct path (DP) time of arrival (TOA) of the signals received from 4G long term evolution (LTE) cellular base stations (BSs) in a mobile multipath environment. This is important for TOA-based ranging measurements, which may be exploited for positioning applications. A sub-space approach is used for the estimation of the multipath time of arrival, and a state-space approach is exploited for tracking the direct path. The developed framework is applied to real LTE signals collected\ud using a portable experimental setup during a car drive in the town of Rapperswil, Switzerland. The pseudoranges derived from the tracking of the DP TOA are then compared to the ranges from the considered LTE base stations calculated using GPS, demonstrating the effectiveness of the proposed approach

This paper presents an experiment using real Long-Term Evolution (LTE) signals to extend positioning from outdoors to indoors. LTE signals are of interest for positioning applications because of their availability indoors, where GNSS signal reception is limited. Different approaches for time of arrival (TOA) extraction are evaluated for their positioning performance, combined with an extended Kalman filter (EKF) for movement tracking. The paper shows that the performance is surprisingly good, with high visibility of cellular signals even in the difficult indoor test environment, and with a positioning error once indoors smaller than 8m in 50% of cases

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