Error Mitigation for TDoA UWB Indoor Localization using Unsupervised Machine Learning
This addresses accuracy issues in indoor positioning systems for applications like robotics or navigation, but it is incremental as it builds on existing UWB and clustering techniques.
The paper tackled positioning errors in UWB indoor localization caused by multi-path fading by proposing an unsupervised anchor node selection method using deep embedded clustering, resulting in a 23.1% reduction in mean absolute error and up to 49.3% reduction in 95th percentile error in dense areas.
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.