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Terminal location method with NLOS exclusion based on unsupervised learning in 5G‐LEO satellite communication systems
Author(s) -
Zhu Feng,
Ba Teer,
Zhang Yuan,
Gao Xiqi,
Wang Jiang
Publication year - 2020
Publication title -
international journal of satellite communications and networking
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.388
H-Index - 39
eISSN - 1542-0981
pISSN - 1542-0973
DOI - 10.1002/sat.1346
Subject(s) - non line of sight propagation , computer science , gnss applications , pseudorange , satellite , cluster analysis , terminal (telecommunication) , telecommunications link , global positioning system , real time computing , algorithm , artificial intelligence , wireless , telecommunications , engineering , aerospace engineering
Summary We investigate the terminal location method in 5G‐Low Earth Orbit (5G‐LEO) satellite communication systems. To overcome the dependence on the external Global Navigation Satellite System (GNSS), we propose to use a single LEO satellite in 5G‐LEO satellite communication systems for terminal location and utilize the downlink synchronization detection for pseudorange differential measurement. Then, a data clustering method of unsupervised machine learning is proposed to classify the measured data into line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS) signals. Furthermore, the NLOS data are excluded, and the Taylor series expansion iteration method is used to calculate the terminal coordinates. Simulation results show that the proposed method can effectively reduce the influence of NLOS error on measurement results and improve the accuracy of terminal location. In simulated urban scenario, the average location accuracy is improved from 10 km by traditional methods to 0.7 km and the convergence time is reduced from 400 to 250s.