Indoor target localization is an essential and fundamental issue for wireless sensor networks (WSN). However, it\r\nis rather difficult for WSN to maintain the localization accuracy in line-of-sight (LOS) and non-line-of-sight (NLOS)\r\nmixed environment. NLOS propagation always leads to larger ranging error than LOS does. When the target\r\nmoves in the rooms and corridors, the signal transmission state will switch frequently between LOS and NLOS.\r\nIt is a challenging task to deal with this situation because the ranging error characteristics under LOS and NLOS\r\nconditions are quite different. In this paper, we propose an interacting multiple model-extended Kalman filter\r\n(IMM-EKF) algorithm to improve the localization accuracy for moving target in indoor environment. In the IMM\r\nstructure, two Kalman filters (KF) are adopted in parallel to accurately smoothen the distance measurement. The\r\nproposed algorithm can adapt to the dynamically changing condition between LOS and NLOS due to the two\r\nKFs'' interaction so that large NLOS ranging errors are further reduced. Once the estimated ranges are obtained,\r\nthe EKF is employed to estimate the target''s location. Empirical measurement results are obtained from typical\r\noffice environment to verify the effectiveness of the proposed algorithm. Experimental results illustrate that the\r\nIMM smoother can efficiently mitigate the NLOS effects on ranging errors and achieve high localization accuracy.
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