New Approach of High Sensitivity Techniques Using Collective Detection Method with Multiple GNSS Receivers
Bibliographic record
Abstract
The Collective Detection (CD) technique is a promising approach to meet the requirements for signal acquisition in GNSS-harsh environments. The CD approach has been proposed because of its potential to operate as both a direct positioning method and a high-sensitivity acquisition method. This paper is dedicated to the development of a new CD architecture for processing satellite signals in challenging environments. It proposes the best signal acquisition method used according to the reception conditions of the different receivers that can assist the user in difficulty. Knowing that the CD approach is beneficial in the case where the maximum of satellite signals can be combined, the proposed approach consists in choosing the best receiver(s) from several connected receivers to serve as a reference station, as smart cooperative navigation concept. New metrics of the CD with optimal weighting of visible satellites are exploited. Analysis of optimization method in order to use better satellites according to some defined parameters (elevation, C / N 0 , and GDOP) were carried out. Real GPS L1 C/A signals are exploited to analyze the efficiency of the proposed approach. A comparison of the results through the accumulation of some good satellites among all visible satellites have shown the effectiveness of this method.
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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 | 0.001 |
| 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".