Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
Bibliographic record
Abstract
Although antenna array-based Global Navigation Satellite System (GNSS) receivers can be used to mitigate both narrowband and wideband electronic interference sources, measurement distortions induced by array processing methods are not suitable for high precision applications. The measurement distortions have an adverse effect on the carrier phase ambiguity resolution, affecting the navigation solution. Depending on the array attitude information availability and calibration parameters, different spatial processing methods can be implemented although they distort carrier phase measurements in some cases. This paper provides a detailed investigation of the effect of different array processing techniques on array-based GNSS receiver measurements and navigation performance. The main novelty of the paper is to provide a thorough analysis of array-based GNSS receivers employing different beamforming techniques from tracking to navigation solution. Two beamforming techniques, namely Power Minimization (PM) and Minimum Power Distortionless Response (MPDR), are being investigated. In the tracking domain, the carrier Doppler, Phase Lock Indicator (PLI), and Carrier-to-Noise Ratio (C/N₀) are analyzed. Pseudorange and carrier phase measurement distortions and carrier phase position performance are also evaluated. Performance analyses results from simulated GNSS signals and field tests are provided.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".