This paper presents robust localization algorithms that use range measurements to estimate the location parameters. The non-line-of-sight (NLOS) propagation of a signal can severely deteriorate the estimation performance in indoor and population-dense urban areas. Therefore, the robust localization algorithms are considered in this paper. In particular, the robust statistics-based localization is dealt with. The maximum likelihood (ML)-type and multi-stage ML-type method-based weighted least squares (WLS) algorithms and robust extrapolated single propagation unscented Kalman filter (ESPUKF) are proposed for mixed line-of-sight (LOS)/NLOS environments. Based on extensive simulations, the positioning accuracies of the proposed methods are found to be superior to those of conventional methods in the mildly and moderately mixed LOS/NLOS conditions. In addition, analyses are conducted on the mean square error (MSE), asymptotical unbiasedness and computational complexity of the proposed algorithms.

中文翻译：

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混合 LOS/NLOS 条件下基于 ML 型、多级 ML 型和外推单传播 UKF 方法的鲁棒定位

本文介绍了使用距离测量来估计位置参数的稳健定位算法。信号的非视距 (NLOS) 传播会严重降低室内和人口密集城市地区的估计性能。因此，本文考虑了稳健的定位算法。特别是，处理了强大的基于统计的本地化。针对混合视距 (LOS)/NLOS 提出了基于最大似然 (ML) 型和多级 ML 型方法的加权最小二乘法 (WLS) 算法和稳健的外推单传播无迹卡尔曼滤波器 (ESPUKF)环境。基于广泛的模拟，发现所提出方法的定位精度在轻度和中度混合 LOS/NLOS 条件下优于传统方法。