Indoor positioning systems based on Ultra-wideband (UWB) technology are gaining recognition for their ability to provide cm-level localization accuracy. However, these systems often encounter challenges caused by dense multi-path fading, leading to positioning errors. To address this issue, in this letter, we propose a novel methodology for unsupervised anchor node selection using deep embedded clustering (DEC). Our approach uses an Auto Encoder (AE) before clustering, thereby better separating UWB features into separable clusters of UWB input signals. We furthermore investigate how to rank these clusters based on their cluster quality, allowing us to remove untrustworthy signals. Experimental results show the efficiency of our proposed method, demonstrating a significant 23.1% reduction in mean absolute error (MAE) compared to without anchor exclusion. Especially in the dense multi-path area, our algorithm achieves even more significant enhancements, reducing the MAE by 26.6% and the 95th percentile error by 49.3% compared to without anchor exclusion.

翻译：基于超宽带（UWB）技术的室内定位系统因其能够提供厘米级定位精度而受到广泛认可。然而，这类系统常因密集多径衰落导致定位误差。为解决该问题，本文提出一种基于深度嵌入聚类（DEC）的无监督锚节点选择新方法。该方法在聚类前使用自编码器（AE），从而将UWB输入特征更有效地分离为可聚类簇。此外，我们进一步研究如何根据簇质量进行排序，从而剔除不可信信号。实验结果表明，所提方法具有高效性：与未排除锚节点相比，平均绝对误差（MAE）显著降低23.1%。尤其在密集多径区域，本算法效果更为突出——与未排除锚节点相比，MAE降低26.6%，第95百分位误差降低49.3%。