New Strategy of Collaborative Acquisition for Connected GNSS Receivers in Deep Urban Environments
Bibliographic record
Abstract
Collaborative Positioning (CP) is a better localization technique used to locate a user in challenged environments, which is driven by the increasing presence of cellular phones and mobile devices in urban areas. The basic idea is that the mobile devices can cooperate with each other to improve their ability to determine their position. In this concept, a network of GNSS (Global Navigation Satellite System) receivers can collectively receive available satellite signals, and each receiver can receive signal measurements from other receivers via a communication link. This work shows how to use the Collective Detection (CD) approach to deal with the concept of collaborative or cooperative positioning. Specifically, this paper develops a new strategy allowing a receiver in deep urban environment to locate using the CD approach, while overcoming the implementation complexity problem. The idea consists in applying the CD approach in the case of multiple GNSS receivers to assist a receiver in a difficult situation. A typical case of two connected receivers assisting a receiver in difficulty in a deep urban area shows the effectiveness of this strategy. This strategy is tested with real GNSS signals to analyze its feasibility. The overall gain in complexity can reach up to 46% of what has been achieved in previous works.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".