Wireless indoor localization technology is a hot research field at present. Its basic principle is to estimate the geometric position of the mobile node by measuring the characteristic parameters of the propagation signal between the mobile node and the beacon node. However, in the process of position estimation, there are non-line-of-sight errors such as multipath propagation, which greatly reduces the localization accuracy. This paper proposes an enhanced closest neighbor data association approach based on ultra-wide band (UWB) measurement. First, the measured values were grouped to obtain a series of undetermined prediction position points, and the undetermined points were put into our set verification gate for screening. Then, the particle filter was introduced to weight and redistribute the position estimation after screening, removing the NLOS-contaminated location estimation from consideration. The position estimation group with low error was finally confirmed and weighted again by the nearest neighbor association algorithm. Simulation results showed that the average localization accuracy of the proposed method was about 1 m. Compared with the existing localization algorithms, the proposed method can successfully reduce the influence of NLOS error and obtain higher localization accuracy.

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Abstarct. The indoor positioning based on wireless sensor networks (WSN) has become one of the research hotpots. However, the NLOS propagation of the distance signals greatly challenges the accuracy and robustness of the algorithm. In this paper, we take the suppression of NLOS as the core goal and proposed the FCM-REKF-based positioning method. We firstly identify the signal states through the fuzzy c-means clustering (FCM), for the measurement distance judged to be NLOS, a refactoring method based on FCM is used. Then the corrected distance is smoothed by Kalman filter, and the Robust Extended Kalman Filter is used to calculate the final position. The simulation results show that our method has higher accuracy than EKF, REKF and IMM-EKF under NLOS environment.