Constructing Context-Aware GNSS Stochastic Model for Code-Based Resilient Positioning in Urban Environment
Bibliographic record
Abstract
The global navigation satellite system (GNSS) positioning performance is widely recognized to degrade in challenging environments due to complex observation uncertainty and variability. In GNSS positioning framework, the stochastic model plays a crucial role in achieving unbiased parameter estimation by correctly capturing statistical observation characteristics. However, classical empirical stochastic models, such as elevation-dependent and carrier-to-noise ratio (C/N0)-based weighting schemes, are deemed inadequate for kinematic positioning services. The environmental context, which comprises terminal space and received signal characteristics, possesses the potential to promote quantifying observation uncertainty. To address this issue, we propose to construct context-aware adaptive GNSS stochastic models that integrate context information. Firstly, leveraging a substantial urban vehicle GNSS dataset, code residuals are accurately extracted using high-accuracy reference trajectory, with significant context features extracted and selected. Secondly, a temporal neural network (TNN) is developed for urban scenario recognition, including open sky, urban canyon, boulevard, and under viaduct. Additionally, context-aware stochastic functions are formulated through correlation analysis and function fitting between C/N0 and median code residuals. Finally, the context-aware stochastic model can be adaptively configured with context prediction results yield by TNN. Experimental results show the context detection model achieves an high-confidence accuracy of 95.37%. Furthermore, the proposed method is evaluated and compared with the classical weighting schemes using code-based single point positioning (SPP). Remarkably, the context-aware stochastic model surpasses the classical C/N0-based stochastic model in terms of continuity and accuracy, representing improvements of 17.60 and 22.39% for horizontal and vertical components, respectively, thereby highlighting its remarkable environment adaptability.
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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".