Error Mitigation for TDoA UWB Indoor Localization using Unsupervised
  Machine Learning
Phuong Bich Duong Ben Van Herbruggen Arne Broering Adnan Shahid Eli De Poorter
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.