Distributed and cooperative estimation of formation flight of unmanned vehicles subject to faults and unreliable information
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, a novel framework for cooperative estimation of formation flight of unmanned vehicles subject to presence of actuator faults is proposed. This framework is developed based on the notion of sub-observers where within a group of sub-observers each sub-observer is estimating certain states that are conditioned on a given input, output, and state information. We model the overall estimation process by a weighted estimation (WE) digraph. By selecting an appropriate path in the weighted estimation digraph, an assigned supervisor can select and configure a set of sub-observers to successfully estimate all the system states. In presence of large disturbances, noise, and faults certain sub-observers may become invalid (or unreliable), and consequently the supervisor reconfigures the set of sub-observers by selecting a new path in the estimation digraph such that the impacts of these anomalies are confined to only the local estimators. This will prevent the propagation of the uncertainties on the estimation performance of the entire formation flight system. Simulations are conducted on a five satellite formation flight system in deep space where the results confirm the validity of our analytical work.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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 it