Interference and multipath mitigation utilising a two‐stage beamformer for global navigation satellite systems applications
Bibliographic record
Abstract
The performance of location‐based services provided by global navigation satellite systems is compromised by interference and multipath propagations. Although time/frequency interference suppression methods have been widely studied in the literature, they fail to cope with wideband interference signals. Instead, techniques utilising several antenna elements can be employed to mitigate both narrowband and broadband interference signals. However, the performance of beamforming techniques utilising antenna arrays severely degrades in dealing with correlated and coherent multipath components which cause signal cancellation phenomenon and temporal correlation matrix rank deficiency. This study proposes a two‐stage beamformer to jointly deal with interference and multipath signals. In the first stage, before the despreading process, by applying the subspace method, the interference subspace is estimated and used as a constraint for the optimisation problem in the next stage. In the second stage, a modified version of the minimum power distortionless response beamformer employing several overlapping sub‐arrays called the minimum difference output power method is utilised to mitigate the correlated multipath components. The proposed beamformer can deal with the signal cancellation phenomenon and temporal correlation matrix rank deficiency. Several simulation examples and a real data test are provided to illustrate the effectiveness of the proposed beamformer. Results show that the proposed method is able to put deep nulls in the direction of the narrowband and wideband interference signals, and significantly reduces the multipath‐induced time of the arrival error.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".