Cooperative State and Fault Estimation of Formation Flight of Satellites in Deep Space Subject to 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 distributed cooperative estimation framework for a formation flight of satellites is proposed. This framework is developed based on the notion of sub-observers. Within a group of sub-observers each one is estimating certain states that are conditioned on a given input, output, and state information. In order to guarantee the ultimate boundedness of the estimation errors, a sub-observer dependency (SOD) digraph is introduced that is assumed to be acyclic. The overall estimation process is modeled by a weighted sub-observer dependency estimation (WSODE) digraph. By selecting an optimal path in the WSODE digraph, a high-level supervisor can then select and configure a set of sub-observers to successfully estimate all the system states. In presence of unreliable information due to large disturbances, noise, and actuator faults certain sub-observers may become invalid. In this case, the supervisor reconfigures the set of sub-observers by selecting a new path in the WSODE digraph such that the impacts of these uncertainties are managed and confined to only the local estimates of states and faults. This will consequently prevent the propagation of uncertainties to the entire estimation process and the performance degradations to the entire formation flight of satellites. Simulations are conducted for a five satellite formation flight system in deep space and the comparative results with a centralized Kalman filter (CKF) technique are shown to confirm the validity and advantages of our developed analytical work.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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