Research On LTE Terminal Positioning

With the rapid development of mobile Internet, applications based on mobile terminal positioning service spring up in various areas like digital maps, food guides, traffic information, rescue positioning. To meet users’demand of such services, it is necessary to guarantee the mobile terminal positioning’s accuracy. As a promising next generation of mobile communication system, LTE will be the important network of carrying mobile data services. Thus, it is of realistic significance to research on LTE terminal positioning.At first, this paper introduces the main characteristics and advantages of LTE system, analyses the status quo and mainstream technology of terminal positioning in mobile communication network, and then studies the factors affecting terminal positioning’s accuracy, NLOS environment’s error model and evaluation index for positioning accuracy. Thus provides the theoretical basis for LTE terminal positioning’s research. Secondly, this paper systematically analyzes LTE terminal positioning system’s architecture, introduces the network elements’function of the positioning system, and then studies the principles of three positioning methods in LTE control plane and analyses their relevant positioning processes. Then, this paper studies the OTDOA positioning method of LTE in NLOS environment. Based on that, an iterative positioning algorithm which is suitable for NLOS environment is proposed. Compared with the traditional positioning algorithm, it introduces error coefficient to reconstruct OTDOA measurements and reduce the NLOS error instead of using the priori information of channel state to estimate NLOS error’s mean and variance. The optimal error coefficient is iteratively calculated and updated to modify the measurements containing NLOS error, to get close to the LOS value, and the computation complexity is reduced by using hierarchical refinement method. Finally, this paper proposes an E-CID positioning method based on adaptive genetic algorithm in NLOS environment. The positioning method realizes the global search of nonlinear least weight optimization problem by using the biological evolution procedure of Darwinian’s genetic selection and natural selection theory, and dynamically changes the crossover and mutation probability with adaptive genetic algorithm to prevent the convergence to local optimum, and finally balance the population evolution and population fitness. Simulation with MATLAB proves the two algorithms’ positioning performance in NLOS environment.