An Enhanced TDoA Method for 5G Real-Time Indoor Localization with Clustering

Due to the challenging propagation characteristics, 5G positioning signal will be significantly affected when encountering obstacles or reflectors during propagation, resulting in a heavy degradation in positioning accuracy. This paper proposes an enhanced Time Difference of Arrival (TDoA) method for 5G real-time indoor localization with clustering. This unsupervised method can achieve reliable and precise positioning through time alignment error (TAE) calibration, combinational data expansion, two-stage filtering and clustering. An empirical evaluation is conducted using an open dataset generated in a real-world office scenario. Experimental results demonstrate that the proposed method can perform well in environments containing obvious Non-Line-of-Sight (NLoS) conditions. The best algorithm achieves an average accuracy of 0.763 m, which improves 58.3% compared with the classical multi-base station localization algorithm-Weighted Least Squares.