Narrowband Jamming Mitigation Based on Multi-Resolution Analysis for Land Vehicles
Bibliographic record
Abstract
Autonomous and connected vehicles mainly rely on global navigation satellite systems (GNSS) for positioning and navigation, which is a key component for path planning and guidance. It is therefore crucial to ensure the reliability and robustness of the GNSS signals. Jamming has recently become one of the major concerns for GNSS receivers, especially with the widespread of in-car jammers that broadcast jamming signals. Therefore, future vehicle manufacturers must deploy advanced anti-jamming techniques that have to be extensively tested before being deployed in future self-driving vehicles. This motivates us to develop a robust anti-jamming technique based on wavelet packet transform to efficiently suppress jamming signals and enhance the performance of the acquisition, tracking, and navigation stages within a software-defined receiver. The developed technique is computationally efficient, thus, more suitable for real-time processing. Several experiments for different driving scenarios are performed to verify the effectiveness of the proposed method for mitigating various types of jamming signals. Experiments are conducted on global positioning system (GPS) L1 C/A signals obtained from Spirent system for a fully controlled environment. Moreover, a unique experimental setup was developed to assess the performance of the proposed technique in the presence of a jamming signal from a real jammer.
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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 | 0.000 | 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) | 0.001 | 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".