Indoor Localization With Distributed 5G Small Cells Considering Time Alignment Errors

This article addresses high-precision positioning for an indoor positioning system (IPS) made up of distributed 5G small cells. In practical scenarios, the time alignment errors (TAEs) among base transceiver stations (BTSs) can be in the order of tens of ns. While such errors can satisfy the communication requirements, their impact on localization accuracy can be significant and hence needs to be addressed. To this end, we first reformulate the time-of-arrival (TOA) measurement model to account for the TAEs among BTSs. Then, we propose two algorithms to estimate the time difference alignment errors (TDAEs) based on the time difference-of-arrival (TDOA) and time double difference-of-arrival (TDDOA) measurement model utilizing multiple user equipment (UE) positions of historical multisnapshot data. These proposed approaches involve the solution of a weighted nonlinear least-square (WNLS) optimization problem for which the Gauss-Newton iteration method is employed. The effectiveness of these proposed algorithms is verified by using both simulated and real-world data of the distributed 5G IPS. Results of both simulation and real-world experiments show that the positioning accuracy using the proposed method can reach the submeter level. We derive the root Cramer-Rao lower bound (CRLB) for two proposed methods and analyze their performance. Results of both simulation and real-world experiments confirm the theoretical analysis of the estimation performance and reveal the characteristics and advantages of the proposed methods.