A GNSS structural interference mitigation technique using antenna array processing
Bibliographic record
Abstract
Position solutions provided by Global Navigation Satellite Systems (GNSS) can be completely misled by structural interference or spoofing threats. An approach utilizing an antenna array is proposed in order to suppress spoofing attacks. The proposed method is based on the assumption that all spoofing signals are transmitted from a single point source. A spatial domain processing technique is proposed to extract the spoofing signal steering vector and consequently to discard the spoofing signals. This method is implemented before despreading and acquisition stage of a GNSS receiver. Hence, it does not impose a heavy computational load on the receiver operational process since it does not require any extensive search in the code and Doppler domains to separately despread individual authentic and spoofing signals. Moreover, the proposed method does not require any antenna array calibration process. This pre-despreading interference mitigation technique is further extended to maximize signal-to-noise ratio (SNR) of each individual authentic GNSS signal. Simulation results show that the proposed method effectively countermeasures spoofing attacks for a wide range of received spoofing power.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".