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Geolocation and tracking of mobile objects is an important issue in wireless communication networks. Various methods have been devised and implemented to deal with such problems whose performance is particularly limited in non-line-of-sight propagation conditions. In this paper, we take advantage of the extended Kalman filter with some extensions, modifications and improvement of previous work to reduce the NLOS error in the location measurement. One of the key contributions of this paper is to present the methods that discriminate the NLOS measurements from the LOS measurements based on the standard deviation and K-means clustering and reconstruct the LOS measurements out of the NLOS measurements by polynomial fit in order to mitigate the NLOS error. Simulation results confirm the effectiveness and accuracy of our approach in comparison with the conventional EKF algorithm. Moreover, we do not model the distribution of the NLOS error due to its intractability.