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.

基于 Ultra-wideband（UWB）技术的室内定位系统由于其提供厘米级定位精度的能力而受到认可。然而，这些系统经常面临密集多径衰落导致的定位误差挑战。为了解决这个问题，在本信中我们提出了一种利用深度嵌入聚类（DEC）的无监督锚节点选择的新方法。我们的方法在聚类之前使用自动编码器（AE），从而更好地将 UWB 特征分为可分离的 UWB 输入信号聚类。我们进一步研究如何基于聚类质量对这些聚类进行排序，从而能够去除不可靠的信号。实验结果显示了我们提出的方法的效率，相比不排除锚点，平均绝对误差（MAE）显著减少了 23.1%。尤其在密集多径区域，我们的算法实现了更显著的改进，将 MAE 减少了 26.6%，将 95th 百分位误差减少了 49.3%。

使用无监督机器学习进行 TDoA 超宽带室内定位的误差修正

Error Mitigation for TDoA UWB Indoor Localization using Unsupervised  Machine Learning

Ultra-wideband technology has become increasingly popular for indoor
localization and location-based services. This has led recent advances to be
focused on reducing the ranging errors, whilst research focusing on enabling
more reliable and energy efficient communication has been largely unexplored.
The IEEE 802.15.4 UWB physical layer allows for several settings to be selected
that influence the energy consumption, range, and reliability. Combined with
the available link state diagnostics reported by UWB devices, there is an
opportunity to dynamically select PHY settings based on the environment. To
address this, we propose a deep Q-learning approach for enabling reliable UWB
communication, maximizing packet reception rate (PRR) and minimizing energy
consumption. Deep Q-learning is a good fit for this problem, as it is an
inherently adaptive algorithm that responds to the environment. Validation in a
realistic office environment showed that the algorithm outperforms traditional
Q-learning, linear search and using a fixed PHY layer. We found that deep
Q-learning achieves a higher average PRR and reduces the ranging error while
using only 14% of the energy compared to a fixed PHY setting in a dynamic
office environment.

该研究旨在提高 UWB 无线通信的可靠性和能效，通过动态选择物理层设置，在真实的办公环境中验证了深度 Q-learning 方法与传统 Q-learning、线性搜索和使用固定物理层相比，能够实现更高的数据包接收率和更少的能量消耗。