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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Summary Wireless sensor network (WSN) is formed with numerous communication nodes, which plays an important role in the Internet of Things (IoT). Location‐based service is critically important for WSN; however, the nonline of sight (NLOS) condition can deteriorate the positioning precision significantly. In this paper, a robust localization algorithm based on the range measurements to deal with the mixed line of sight (LOS)/NLOS environment is proposed. Firstly, a hypothesis testing based on the data deviation is used to detect the transmission condition. For the identified LOS propagation, the measured data are processed by Gaussian mixture model (GMM) to calculate the mean and standard deviation, which can update the likelihood probability and residual by data fusion. Moreover, for the identified NLOS propagation data, the mean of every Gaussian component is processed by gray Kalman filter (GKF) to discard large outliers. Finally, maximum likelihood (ML) is adopted to derive final coordinates. The simulation results have shown that the proposed algorithm has great advantages in dealing with severe NLOS interference and has higher accuracy compared with existing classic algorithms.