As the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the growing non-line-of-sight error. It is a fatal impact for the localization accuracy of the mobile target. In this article, a novel method based on the nearest neighbor variable estimation is proposed to mitigate the non-line-of-sight error. First, the linear regression model of the extended Kalman filter is used to obtain the residual of the distance measurement value. After that, the residual analysis is used to complete the identification of the measurement value state. Then, by analyzing the statistical characteristics of the non-line-of-sight residual, the nearest neighbor variable estimation is proposed to estimate the probability density function of residual. Finally, the improved M-estimation is proposed to locate the mobile robot. Experiment results prove that the accuracy and robustness of the proposed algorithm are better than other methods in the mixed line-of-sight/non-line-of-sight environment. The proposed algorithm effectively inhibits the non-line-of-sight error.

中文翻译：

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一种基于鲁棒扩展卡尔曼滤波器和改进M估计的物联网移动定位方法

无线传感器网络作为物联网的关键技术，近年来受到越来越多的关注。移动定位是无线传感器网络中的重要课题之一。在无线传感器网络中，非视距传播是一种普遍现象，导致非视距误差越来越大。对移动目标的定位精度是致命的影响。在本文中，提出了一种基于最近邻变量估计的新方法来减轻非视距误差。首先，利用扩展卡尔曼滤波器的线性回归模型得到距离测量值的残差。之后，利用残差分析完成对测量值状态的识别。然后，通过分析非视距残差的统计特征，提出了最近邻变量估计来估计残差的概率密度函数。最后，提出了改进的 M 估计来定位移动机器人。实验结果证明，在混合视距/非视距环境下，所提算法的准确性和鲁棒性优于其他方法。该算法有效抑制了非视距误差。