A Comprehensive Study of the Effects of Linear Chirp Jamming on GNSS Receivers under High-Dynamic Scenarios
Bibliographic record
Abstract
Despite the significant advances in signal processing methods used nowadays, Global Navigation Satellite Systems (GNSS) receivers still experience substantial challenges, such as signal jamming, which remains a crucial source of degradation of the receiver performance. The presence of jamming signal influences the acquisition and tracking modules inside the receiver leading to loss-of-lock of the GNSS satellite signals. Consequently, GNSS receivers cannot provide reliable position, velocity and time services. The aim of this paper is to comprehensively explore the effects of linear chirp jamming on commercial receivers under high-dynamic scenarios. Moreover, the paper investigates the advantages of using combined GPS/GLONASS receivers under jamming conditions in comparison to using GPS-only receivers. In this paper, a SPIRENT GSS6700 Multi-GNSS Simulator controlled by Spirent SimGEN™ software is used to provide realistic controlled simulation scenarios. The linear chirp jamming signals are created using an Agilent interference signal generator (ISG) unit. Both commercial NovAtel ProPak-G2 Plus and NovAtel OEMV receivers are used to conduct these tests. The results show different behaviors of the various receivers in response to the applied jamming signals. The Carrier-to-Noise (C/N0), the Dilution of Precision (DOP), and the navigation solution accuracy are used as measures to assess the performance of the receivers under study. Results show that the NovAtel OEMV receiver outperforms the NovAtel ProPak-G2 Plus receiver. Moreover, it is revealed that multi-constellation receivers achieved higher resistance for signal jamming effects than GPS only receivers.
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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 | 0.001 |
| 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 | 0.001 | 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".