Benchmarking Transformer and Sequence Models for UWB Indoor Localization
Bibliographic record
Abstract
Accurate indoor localization is a key enabler for many intelligent systems, including autonomous vehicles, service robots, smart infrastructure, and healthcare technologies. Ultra-Wideband (UWB) technology provides high-resolution range measurements and resilience to multipath, making it well-suited for such applications. However, in cluttered indoor environments, reflected signals under non-line-of-sight (NLoS) conditions can distort range estimates. These distortions are encoded in the Channel Impulse Response (CIR), which describes how signals propagate through the environment. This paper presents a comparative benchmark of learning-based models for predicting and correcting UWB ranging errors directly from CIR data. We evaluate three neural architectures—Transformer, BiLSTM, and CNN—within a unified pipeline and integrate their predictions into a classical Weighted Least Squares (WLS) localization algorithm via adaptive error weighting. Experiments on a publicly available UWB dataset spanning four diverse environments show that the Transformer-WLS model achieves the lowest average error in three out of four settings—including 1.08 m in an industrial space—outperforming BiLSTM-WLS (1.22 m), CNN-WLS (4.01 m), and traditional WLS (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{1. 3 3 m}$</tex>). These results highlight the effectiveness of attention-based sequence modeling for UWB localization and provide practical guidance for deploying robust learning-enhanced positioning systems in real-world environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".