一种LOS/NLOS混合环境下无线传感器网络定位方法

A Novel Localization Method for Wireless Sensor Network in Mixed LOS/NLOS Environment

非视距传播是无线传感器网络定位中的难题,尤其在复杂的室内环境中,由于视距(Line of Sight,LOS)和非视距(Non-Line of Sight,NLOS)环境的不断切换,对移动目标的精准定位仍是一项难题。文中提出了一种基于平方根无迹卡尔曼滤波和鲁棒扩展卡尔曼滤波并行工作的定位方法,首先对所有节点同一时刻多次采样的TOA测量值进行是否处于非视距环境的判别,如果处于LOS环境,则使用平方根无迹卡尔曼滤波算法进行动态跟踪,若某一节点处于NLOS环境,则采用高斯混合多模型算法对多次采样值进行聚类,并将聚类结果送入并行工作的2个鲁棒扩展卡尔曼滤波器和无迹卡尔曼滤波器,经概率加权后输出定位结果。理论分析和仿真结果表明,本方法具有良好的定位精度。

Non-line-of-sight propagation is an important issue in the localization of wireless sensor networks. Especially in the indoor environment,the precise positioning of the moving target is still a problem due to the continuous switching of the line of sight( LOS) and non-line of sight( NLOS) environment. In this paper,a localization method based on square root unscented Kalman filter( SRUKF) and robust extended Kalman filter( REKF) is proposed. Firstly,judge whether the TOA measurement values of all nodes sampled at the same time are non-line-of-sight or not. If it is in the LOS environment,SRUKF is used for dynamic tracking. If a node is in the NLOS environment,the Gaussian mixture model( GMM) is used to cluster multiple samples. The results of two robust extended Kalman filters and unscented Kalman filters are weighted by probability and finally output the localization result. The theoretical analysis and simulation results show that the method has a great position accuracy.