Parameter Precise Estimation Technology of Active Segment of Non-cooperative Targets Based on Long Short-Term Memory
Bibliographic record
Abstract
Traditional algorithms do not fully utilize the timing information of non-cooperative targets, and setting too many motion parameters can lead to complex dynamic model calculations. This paper proposes a long short-term memory (LSTM) network-based method for estimating the parameters of the active segment of the non-cooperative target under single-satellite observation. Based on the simulation training set of the active segment of the non-cooperative target, the network parameters of the LSTM network are designed, the motion characteristics of the active segment of the non-cooperative target are fully excavated through data-driven methods, and the candidate cutting trajectories are screened and predicted to realize the estimation of the motion parameters of the active segment of the non-cooperative target under the condition of single-satellite observation. The experimental results show that the estimation method proposed in this paper can effectively deal with the inaccurate problem with the non-cooperative target’s active segment motion model established under the condition of single-satellite observation, obtain more accurate active segment motion parameters, and provide a feasible new idea and method for the parameter estimation of the active segment of the non-cooperative target under the single-satellite observation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".