Cooperative Localization for UAV Systems From the Perspective of Physical Clock Synchronization
Bibliographic record
Abstract
The positioning accuracy determines the scope of the application of an unmanned aerial vehicle (UAV). In view of the existing UAV cooperative localization methods that normally require prior information and the assistance of external systems, such as the global positioning system (GPS), this study aims to adopt range radios to measure the time-of-arrival (TOA) information among UAVs and then perform clock synchronization and cooperative localization based on ranging measurements. We propose a framework to jointly estimate the clock error and relative distance, adjust the onboard clock, and perform relative positioning. To achieve autonomous clock synchronization and ranging, a practical approach based on peer-to-peer pseudorange measurements is proposed in this study. We modeled the synchronous two-way ranging (STWR) process using a discretetime state-space model, according to which a linear parameter estimation method and clock steering method are presented. Finally, a closed loop consisting of STWR, parameter estimation, and clock tuning is constructed to improve the ranging accuracy, which leads to improved localization accuracy. Simulation results show that the proposed approach outperforms existing methods and can achieve sub-nanosecond-level time synchronization and meter-level cooperative localization.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".