Space Noncooperative Target Trajectory Tracking Based on Maneuvering Parameter Estimation
Bibliographic record
Abstract
The space noncooperative target maneuvering trajectory tracking is essential for the safety of the on-orbit spacecraft. For the noncooperative target, the maneuvering model is complex and changeable. Besides, the maneuvering dynamics model, the operation time, and the maneuvering frequency are previously unknown. It is difficult to achieve high-precision maneuvering trajectory tracking. In this paper, a novel real-time maneuvering trajectory tracking algorithm is developed, in which the maneuvering trajectory of the noncooperative target is discretized first, and then the differential algebra method is utilized to estimate the maneuvering parameter of the noncooperative target in the discretized time. Since the discretized period is very short, the maneuvering parameters of the target in the next discretized time are assumed to be the same as those in the previous discretized time, and the estimated maneuvering parameters are utilized to predict the target’s relative state in the next discretized time to achieve maneuvering trajectory tracking. Compared with the interactive multimodel method (IMM), the proposed method can estimate the maneuvering parameter of the noncooperative target in real time, which greatly reduces the tracking error caused by the mismatching of the target’s maneuvering model. In order to verify the effectiveness of the algorithm, a simulation of a noncooperative target’s maneuvering trajectory tracking is provided. The results demonstrated that the proposed method could track the noncooperative target maneuvering in real time, and the estimation accuracy was improved by about 93.07% compared with the IMM.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".