Wireless sensor network (WSN) is a self-organizing network which is composed of a large number of cheap microsensor nodes deployed in the monitoring area and formed by wireless communication. Since it has the characteristics of rapid deployment and strong resistance to destruction, the WSN positioning technology has a wide application prospect. In WSN positioning, the nonline of sight (NLOS) is a very common phenomenon affecting accuracy. In this paper, we propose a NLOS correction method algorithm base on the time of arrival (TOA) to solve the NLOS problem. We firstly propose a tendency amendment algorithm in order to correct the NLOS error in geometry. Secondly, this paper propose a particle selection strategy to select the standard deviation of the particle swarm as the basis of evolution and combine the genetic evolution algorithm, the particle filter algorithm, and the unscented Kalman filter (UKF) algorithm. At the same time, we apply orthogon theory to the UKF to make it have the ability to deal with the target trajectory mutation. Finally we use maximum likelihood localization (ML) to determine the position of the mobile node (MN). The simulation and experimental results show that the proposed algorithm can perform better than the extend Kalman filter (EKF), Kalman filter (KF), and robust interactive multiple model (RIMM).

中文翻译：

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混合LOS / NLOS环境下无线传感器网络的鲁棒室内移动定位算法。

无线传感器网络（WSN）是一个自组织网络，由部署在监视区域中并通过无线通信形成的大量廉价微传感器节点组成。WSN定位技术具有部署迅速，抗破坏性强的特点，具有广阔的应用前景。在WSN定位中，视线（NLOS）是影响准确性的非常常见的现象。在本文中，我们提出了一种基于到达时间（TOA）的NLOS校正方法算法来解决NLOS问题。我们首先提出一种趋势修正算法，以校正几何中的NLOS误差。其次，提出了一种粒子选择策略，以粒子群的标准偏差作为进化的基础，并结合遗传进化算法，粒子滤波器算法和无味卡尔曼滤波器（UKF）算法。同时，我们将正交理论应用于UKF，使其具有处理目标轨迹突变的能力。最后，我们使用最大似然定位（ML）来确定移动节点（MN）的位置。仿真和实验结果表明，该算法的性能优于扩展卡尔曼滤波器（EKF），卡尔曼滤波器（KF）和鲁棒交互式多重模型（RIMM）。