Fixed-Time Distributed Position Estimation of Multiagent Systems Based on Local Bearing Measurement
Bibliographic record
Abstract
This article addresses the localizability of local-bearing-based multiagent systems without common orientation, which are more general but also more challenging than global-bearing-based multiagent systems. A novel local-bearing-based fixed-time orientation estimation algorithm is first proposed for orientation alignment. Local bearing information is more easily obtained than the global one, making the new orientation estimation result applicable in a wider range of scenarios. Combined with the orientation estimation algorithm, a fixed-time distributed position estimation algorithm is proposed to realize the accurate localization estimation. Leveraging the cascade system, the global convergence of the proposed estimation scheme is derived using a mathematical induction method. A distinctive advantage of the estimation scheme is that it can realize orientation alignment and accurate global absolute position estimation merely using the local bearing measurements. Some simulation and experimental verification results are provided to prove the effectiveness of the proposed estimation algorithms.
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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 | 0.001 | 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".