A Model-Based Drift Correction Control for UAV in GNSS-Degraded Environments
Bibliographic record
Abstract
Among its many applications, Global Navigation Satellite System (GNSS) systems made an entrance into the Unmanned Aerial Vehicle (UAV) field with positioning and navigation. These applications usually require high positioning accuracy out of safety considerations. Moreover, UAV applications are often found in dense urban or forested areas with poor reception conditions for each available satellite signal, and, thus, degrades the overall accuracy of GNSS positioning. In particular, the position accuracy deteriorates in the face of environmental obstructions as services and multi-path receptions become unavailable. Our works consist of two parts. The first is a nominal Linear Quadratic Gaussian (LQG) controller that estimates the states and tracks the reference trajectory without GNSS drift. The LQG control determines an output feedback law that is optimal in minimizing the expected value of a quadratic cost criterion when the output measurements are corrupted by Gaussian white noise. However, when the reception state is Non-Line Of Sight (NLOS), the Gaussian distribution assumption is no longer valid. Therefore, the latter part of our work contributes a robust controller H∞that estimates the drift and corrects the trajectory accordingly. H∞control applies classical loop-shaping concepts to the multivariable frequency response for good robust performance. Given the estimate of GNSS drift as an input, H∞control not only improves the overall robustness of the control loop but also corrects the drift to converge ground truth to the reference trajectory.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".