Attention-GANs: An Advanced GNSS Data Augmentation Method for Improved NLOS/LOS Classification
Bibliographic record
Abstract
Abstract. Global Navigation Satellite System (GNSS) positioning in urban environments remains challenging due to signal obstructions and reflections caused by tall buildings, trees, and overpasses. Non-Line-of-Sight (NLOS) propagation leads to significant positioning errors, making accurate classification of Line-of-Sight (LOS) and NLOS signals essential for robust GNSS performance. Machine learning (ML) techniques have been widely explored for NLOS/LOS classification, yet their effectiveness is constrained by data imbalance, as acquiring labeled NLOS data is more challenging than LOS data. This imbalance reduces model generalization, leading to biased predictions. To address this challenge, we propose an Attention-GAN framework for synthetic GNSS data generation, coupled with a transformer-based encoder to enhance feature extraction. The proposed Attention-GAN incorporates Multi-Head Self-Attention (MHA) in both its generator and discriminator to improve the quality of generated data. Using the UrbanNav dataset, we validate our approach by training various ML classifiers on augmented data and comparing their performance against conventional methods. Experimental results demonstrate that our approach effectively mitigates data imbalance, improves classification accuracy, and enhances GNSS positioning robustness in complex urban 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".