In a non-line-of-sight (NLOS) environment, ultra-wide band (UWB) high accuracy positioning has been one of the hot topics in studying indoor positioning. In this paper, a factor graph-based UWB positioning algorithm has been proposed based on an improved Turkey robust kernel. It has overcome not only the defect of the least squares algorithm for UWB positioning against non-Gaussian noise but also eliminated the shortcoming of Turkey robust kernel against over-optimization. Aiming at the character of UWB data generally larger than its true value due to barriers, robust kernel will be added into merely big ranging data. However, due to the presence of small ranging data possibly caused by error positioning, the squares of residuals will be taken as the optimized objective function. The experimental result proves that UWB positioning algorithm based on the improved Turkey robust kernel outperforms ordinary UWB positioning algorithms in NLOS environments, with the average positioning accuracy improved by around 20–30%.

在非视距（NLOS）环境中，超宽带（UWB）高精度定位已成为研究室内定位的热门话题之一。本文提出了一种基于土耳其图鲁棒核的基于因子图的UWB定位算法。它不仅克服了针对非高斯噪声的UWB定位的最小二乘算法的缺陷，而且还消除了土耳其鲁棒内核针对过度优化的缺点。针对由于障碍而导致的UWB数据通常大于其真实值的特性，仅将强大的内核添加到较大的测距数据中。但是，由于可能由错误定位引起的小范围数据的存在，残差的平方将作为优化的目标函数。