Ultra-wideband (UWB) time difference of arrival(TDOA)-based localization has emerged as a low-cost and scalable indoor positioning solution. However, in cluttered environments, the performance of UWB TDOA-based localization deteriorates due to the biased and non-Gaussian noise distributions induced by obstacles. In this work, we present a bi-level optimization-based joint localization and noise model learning algorithm to address this problem. In particular, we use a Gaussian mixture model (GMM) to approximate the measurement noise distribution. We explicitly incorporate the estimated state's uncertainty into the GMM noise model learning, referred to as uncertainty-aware GMM, to improve both noise modeling and localization performance. We first evaluate the GMM noise model learning and localization performance in numerous simulation scenarios. We then demonstrate the effectiveness of our algorithm in extensive real-world experiments using two different cluttered environments. We show that our algorithm provides accurate position estimates with low-cost UWB sensors, no prior knowledge about the obstacles in the space, and a significant amount of UWB radios occluded.

翻译：超宽带（UWB）基于到达时间差（TDOA）的定位已发展为一种低成本、可扩展的室内定位方案。然而，在杂波环境中，由于障碍物导致的测量噪声呈现有偏且非高斯分布特性，使得UWB TDOA定位性能下降。本文提出一种基于双层优化的联合定位与噪声模型学习算法以解决该问题。具体而言，我们采用高斯混合模型（GMM）逼近测量噪声分布，并将估计状态的不确定性显式融入GMM噪声模型学习过程（称为不确定性感知GMM），从而同步提升噪声建模与定位性能。首先通过多组仿真场景评估GMM噪声模型学习与定位性能，继而利用两种不同杂波环境下的广泛真实实验验证算法有效性。结果表明：即便采用低成本UWB传感器、无空间障碍物先验知识且存在大量UWB射频信号遮蔽的情况下，本算法仍能提供精准的位置估计。