Mitigation of Ionospheric Scintillation Effects on GNSS Signals Using Variational Mode Decomposition
Bibliographic record
Abstract
This letter addresses the problem of ionospheric scintillation effects on the global navigation satellite system (GNSS) signals. Severe scintillations degrade the signal intensity below the fade margin of the GNSS receiver, resulting in failure of the positioning and navigational services. A robust methodology is needed for the estimation and mitigation of such ionospheric scintillation effects. Hence, in this letter, the application of an adaptive signal decomposition technique based on variational mode decomposition (VMD), in combination with the detrended fluctuation analysis (DFA) method, is reported. VMD-DFA effectively decomposes the GNSS signal affected by ionospheric scintillations into a number of intrinsic mode functions and provides a threshold for the detection and mitigation of scintillations noise. Monte Carlo simulation results demonstrate that the proposed algorithm is superior and reliable for eliminating the amplitude scintillation effects compared to the complementary ensemble empirical mode decomposition method. The application of the proposed algorithm on both synthetic (Cornell scintillation model) and real-time measured GNSS data obtained from GNSS software navigation receiver at Rio de Janeiro, Brazil, has shown its potentiality in mitigating the ionospheric amplitude scintillation effects.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".