A weighted combining method for GPS antenna diversity
Bibliographic record
Abstract
GPS signal detection and parameter estimation are compromised in attenuated and harsh multipath environments. Diversity schemes are viewed as a method to alleviate the multipath fading phenomenon and to enhance signal detection and parameter estimation performance by providing additional processing gain. In this paper, the performance of a weighted diversity combining method for the spatial antenna diversity system is analyzed and compared with the equal gain combining method. The combining methods are performed at two different levels namely the correlator output and the measurement level. The performance of the proposed method is empirically tested with live GPS L1 signal in a harsh indoor environment. Detection performance is assessed through a comparison of Receiver Operating Characteristic (ROC) curves. The parameter estimation accuracies are compared by analyzing the differential pseudorange standard deviation. Accuracy of the local level position solution is also evaluated to investigate the performance at the navigation level. It is shown that the accuracy of positioning by the proposed weighted combining method utilizing the spatial diversity is significantly improved compared to the individual diversity branches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".