Robust localisation techniques that utilise distance observations to determine the location are focused upon. In urban environments with limited visibility and high population density, the presence of non-line-of-sight signals can introduce a positive measurement bias, negatively affecting the accuracy of estimation. To resolve this problem caused by multipath effects, robust localisation techniques have been explored, specifically the skipped filter weighted least squares (WLS) method for localisation. However, the squared estimation bias of the transformed distance estimate of the existing skipped filter WLS method is high in the low signal-to-noise ratio condition owing to the second-order noise terms. Therefore, the modified skipped filter WLS methods are proposed to reduce the squared estimation bias of transformed distance estimate. First, the closed-form modified skipped filter WLS method uses the maximum likelihood estimate (MLE) to reduce the squared estimation bias of the transformed distance estimate. In addition, the modified skipped filter WLS method using the online ML and online expectation maximisation (EM) algorithms are introduced whose advantage is that they do not require the number of Gaussian components unlike the existing Gaussian mixture model EM algorithm. The mean square error analysis of proposed closed-form skipped filter WLS and existing skipped filter WLS methods is performed. Furthermore, the localisation accuracy of the proposed techniques is found to outperform that of competing algorithms via simulation results.

中文翻译：

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基于改进跳过滤波器加权最小二乘算法的鲁棒定位方法

重点关注利用距离观测来确定位置的鲁棒定位技术。在能见度有限和人口密度高的城市环境中，非视距信号的存在可能会引入正测量偏差，从而对估计的准确性产生负面影响。为了解决多径效应引起的这个问题，人们探索了鲁棒的定位技术，特别是用于定位的跳过滤波器加权最小二乘（WLS）方法。然而，由于二阶噪声项，现有跳过滤波器WLS方法的变换距离估计的平方估计偏差在低信噪比条件下较高。因此，提出了改进的跳过滤波器WLS方法来减少变换距离估计的平方估计偏差。首先，封闭式改进的跳过滤波器 WLS 方法使用最大似然估计 (MLE) 来减少变换距离估计的平方估计偏差。此外，还引入了使用在线ML和在线期望最大化（EM）算法的改进的跳过滤波器WLS方法，其优点是与现有的高斯混合模型EM算法不同，它们不需要高斯分量的数量。对所提出的封闭式跳过滤波器 WLS 和现有跳过滤波器 WLS 方法进行均方误差分析。此外，通过仿真结果发现所提出的技术的定位精度优于竞争算法。