Correlation-sum-deviation ranging method for vehicular node based on IEEE 802.11p short preamble
Bibliographic record
Abstract
Growth of the traffic flow and traffic accident has raised more and more demands on wireless communication and positioning technologies that can provide new services such as vehicle collision warning and traffic management. Currently, the global navigation satellite system such as global positioning system and BeiDou satellite positioning system is widely used in vehicles and is fairly accurate in flat open areas. However, the global navigation satellite system can only work in line of sight environment, and it fails to operate in non-line of sight tunnels or downtown areas where blockage of satellite signals is frequent. Because of the shortages of global navigation satellite system, the wireless ranging or positioning system using the short preamble of IEEE 802.11p is provided. Typically, accurate time of arrival estimation is very important for positioning estimation. In order to improve the precision of the time of arrival estimation, a correlation-sum-deviation method for ranging using the IEEE 802.11p short preamble is proposed. Simulation results are presented which show that in both the additive white Gaussian noise channel and the international telecommunications union multipath channel for vehicular environments, the proposed method provides better precision and is less complex than other techniques, particularly when the signal-to-noise ratio is low.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".