XAI GNSS—A Comprehensive Study on Signal Quality Assessment of GNSS Disruptions Using Explainable AI Technique
Bibliographic record
Abstract
The hindering of Global Navigation Satellite Systems (GNSS) signal reception by jamming and spoofing attacks degrades the signal quality. Careful attention needs to be paid when post-processing the signal under these circumstances before feeding the signal into the GNSS receiver's post-processing stage. The identification of the time domain statistical attributes and the spectral domain characteristics play a vital role in analyzing the behaviour of the signal characteristics under various kinds of jamming attacks, spoofing attacks, and multipath scenarios. In this paper, the signal records of five disruptions (pure, continuous wave interference (CWI), multi-tone continuous wave interference (MCWI), multipath (MP), spoofing, pulse, and chirp) are examined, and the most influential features in both the time and frequency domains are identified with the help of explainable AI (XAI) models. Different Machine learning (ML) techniques were employed to assess the importance of the features to the model's prediction. From the statistical analysis, it has been observed that the usage of the SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) models in GNSS signals to test the types of disruption in unknown GNSS signals, using only the best-correlated and most important features in the training phase, provided a better classification accuracy in signal prediction compared to traditional feature selection methods. This XAI model reveals the black-box ML model's output prediction and provides a clear explanation of the specific signal occurrences based on the individual feature contributions. By using this black-box revealer, we can easily analyze the behaviour of the GNSS ground-station signals and employ fault detection and resilience diagnosis in GNSS post-processing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".