Precise Unmanned Aerial Vehicle (UAV) Navigation Using Unscented Kalman Filter (UKF) Based on Genetic Algorithm
Bibliographic record
Abstract
This paper presents a novel algorithm for the accurate navigation of unmanned aerial vehicles (UAV) based on an Unscented Kalman Filter with Genetic Algorithm (GAUKF). It has been demonstrated that this algorithm improves positioning accuracy, and enhances the performance and robustness of the navigation system. GAUKF works by using an adaptive scaling factor for dynamically optimizing noise covariance matrices. GA works on optimizing the scaling factor in this method to improve UAV location stability, lower filtering divergence, and increase estimation accuracy, especially in the presence of uncertain noise. The results indicate that the GAUKF performs better than the regular Unscented Kalman Filter (UKF) and Fuzzy adaptive Kalman Filter(FAUKF) in reducing noise errors and improving UAV positioning and navigation errors. The experimental results for various noise situations confirm the efficacy of this technique.
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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".