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An algorithm for mobile terminal (MT) tracking based on time-of-arrival measurements in non-line-of-sight (NLOS) environments is proposed. It is based on NLOS detection together with a modified probabilistic data association approach where different subgroups of range measurements are constructed. Each of the subgroups provide a position estimate of the MT with it's corresponding covariance matrix that are both used in a hypothesis test for NLOS detection. The accepted position estimates are weighted with different probabilities in a Kalman filter framework. Simulation results show a significant increase in positioning accuracy in NLOS environments with respect to both, the extended Kalman filter (EKF) and a NLOS mitigation algorithm from the literature. In LOS environments similar performance to the EKF is achieved. The proposed method does not assume any statistical knowledge of the NLOS errors and only assumes the sensor noise variance to be known.