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In this study, the location of moving mobile phones with good estimation was investigated by using the cooperative method and Kalman filter. Also, due to GPS limitations, BTS is used. Due to the movement of mobile phones and the use of BTS, a number of BTSs are placed in NLOS, so by maximum density algorithm mitigate NLOS effect

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mobile_localization

In this study, the location of moving mobile phones with good estimation was investigated by using the cooperative method and Kalman filter. Also, due to GPS limitations, BTS is used. Due to the movement of mobile phones and the use of BTS, a number of BTSs are placed in NLOS, so by maximum density algorithm mitigates the NLOS effect

The simulation results of this study have improved compared to similar cases that use GPS or consider users to be static. The reason for this is the use of cooperative methods, NLOS reduction algorithms, and EKF. With a 20 m NLOS error, the error of the BTS estimate is close to 150 m, while the error of the estimate of this research is less than 20 m. By setting the parameters, the error rate of the research estimate has been significantly reduced - and has reached less than 10 meters. In cases LOS and NLOS reach several hundredths, the Kalman filter prevents huge errors affect results. In cases where the BTS estimation error is 2 km, the estimation error of this method is kept below 500 m. Finally, with the help of MATLAB LTE Toolbox, signaling between mobile phones and base stations in the fourth generation is considered.

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In this study, the location of moving mobile phones with good estimation was investigated by using the cooperative method and Kalman filter. Also, due to GPS limitations, BTS is used. Due to the movement of mobile phones and the use of BTS, a number of BTSs are placed in NLOS, so by maximum density algorithm mitigate NLOS effect

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