A New High-Resolution GPS Multipath Mitigation Technique Using Fast Orthogonal Search
Bibliographic record
Abstract
The Delay Locked Loop (DLL) tracking algorithm is one of the most widely used in GPS receivers. It uses different correlators such as the Early-Late Slope (ELS) correlator and High-Resolution Correlator (HRC) to mitigate code phase multipath. These techniques are effective for weak multipath environments but they may not be suitable for challenging multipath environments. The Multipath Estimating Delay Lock Loop (MEDLL) shows better performance than the classical methods. However, MEDLL still has limited capabilities in severe multipath environments. This paper introduces a robust multipath mitigation technique based on fast orthogonal search to obtain better delay estimation for GPS receivers. This research utilised a SPIRENT Global Navigation Satellite Systems (GNSS) simulator to compare the performance of the proposed method with other multipath mitigation techniques. Experimental results demonstrated that the performance of the proposed algorithm was better than the classical and advanced techniques under the multipath scenarios tested.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".