Wavelet Denoising for Amplitude-Modulated Interference Signal Mitigation for GPS Software Receiver
Bibliographic record
Abstract
Recently there is a growing demand for continuous, reliable, and accurate positioning and navigation with the wide spread of a wide range of applications that depend mainly on Global Navigation Satellite System (GNSS), including Global Positioning System (GPS). The widespread of such applications resulted in GPS being an appealing target for jamming. The presence of intentional or unintentional interference is considered one of the major threats to the integrity of the GPS system that not only jeopardizes positioning and timing services but can significantly endanger safety of evolving applications such as autonomous vehicles. This paper aims to propose anti-jamming technique based on wavelet denoising. The proposed technique is assessed based on simulated static and dynamic scenarios obtained from Spirent™ system for a fully controlled environment. The results show that the proposed technique is able to effectively suppress amplitude-modulated (AM) jamming signal.
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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".