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 ($\text{1. 3 3 m}$). 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".