On Robust State Estimation for Practical Problems in Flight Path Reconstruction
Bibliographic record
Abstract
A state estimation method was developed for general flight path reconstruction applications in which the reliability of airdata measurements may vary greatly over time. The method was developed through the application of robust cost functions to the maximum a posteriori estimation philosophy to achieve a trajectory estimation scheme that accurately treats various forms of process noise and time-varying measurement error characteristics that occur in both aircraft system identification and flight path reconstruction applications. It was shown that the estimator is realized as an adaptive maximum a posteriori estimator in which the trajectory and time-varying measurement error covariance matrix are estimated jointly. The efficacy of the scheme was demonstrated through a case study considering its application to simulation data and comparing the performance of implementations realized using a variety of robust cost functions, including a cost function proposed by the authors. A major finding of this work was that implementation based on the authors’ proposed cost function resulted in superior performance. The efficacy of the estimator was further demonstrated though a case study considering its application to real flight-test data. Findings from this case study were consistent with findings from the case study based on simulation data.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".