In the thesis, we propose a non-line of sight (NLOS) mitigation approach based on the interacting multiple model (IMM) algorithm. The IMM-based structure, composed of a biased Kalman filter (BKF) and a Kalman filter with NLOS-discarding process (KF-D), is capable of mitigating the ranging error caused by the NLOS effects, and therefore improving the performance and accuracy in wireless location systems. The NLOS effect on signal transmission is one of the major factors that affect the accuracy of the time-based location systems. Effective NLOS identification and mitigation usually count on pre-determined statistic distribution and hypothesis assumption in the signals. Because the variance of the NLOS error is much large than that of measurement noise, hypothesis testing on the LOS/NLOS status can be formulated.The BKF combines the sliding window and decides the status by using hypothesis testing. The calculated variance and the detection result are used in switching between the biased and unbiased modes in the Kalman filter. In the contrast, the KF-D scheme identifies the NLOS status and tries to eliminate the NLOS effects by directly using the estimated results from the LOS stage. The KF-D scheme can achieve reasonably good NLOS mitigation if the estimates in the LOS status are obtained. Due to the discarding process, changes of the state vector within the NLOS stage are possibly ignored, and will cause larger errors in the state estimates. The BKF and KF-D can make up for each other by formulating the filters in an IMM structure, which could tune up the probabilities of BKF and KF-D. In our approach, the measured data are smoothed by sliding window and a BKF. The variance of data and the hypothesis test result are passed to the two filters. The BKF switches between the biased/unbiased modes by using the result. The KF-D may receive the estimated value from BKF based on the results. The probability computation unit changes the weights to get the estimated TOA values. With the simulations in ultra-wideband (UWB) signals, it can be seen that the proposed IMM-based approach can effectively mitigate the NLOS effects and increase the accuracy in wireless position.