Ultra-wideband (UWB) is the state-of-the-art and most popular technology for wireless localization. Nevertheless, precise ranging and localization in non-line-of-sight (NLoS) conditions is still an open research topic. Indeed, multipath effects, reflections, refractions, and complexity of the indoor radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory position estimation. This article proposes an efficient representation learning methodology that exploits the latest advancement in deep learning and graph optimization techniques to achieve effective ranging error mitigation at the edge. Channel Impulse Response (CIR) signals are directly exploited to extract high semantic features to estimate corrections in either NLoS or LoS conditions. Extensive experimentation with different settings and configurations has proved the effectiveness of our methodology and demonstrated the feasibility of a robust and low computational power UWB range error mitigation.

超宽带（UWB）是最新的，最流行的无线本地化技术。尽管如此，在非视距（NLoS）条件下进行精确测距和定位仍然是一个开放的研究主题。实际上，室内无线电环境的多径效应，反射，折射和复杂性很容易在测距测量中引入正偏差，从而导致高度不准确且不令人满意的位置估计。本文提出了一种有效的表示学习方法，该方法利用了深度学习和图优化技术的最新进展来在边缘实现有效的测距误差缓解。直接利用信道脉冲响应（CIR）信号提取高语义特征，以估计NLoS或LoS条件下的校正。