STAP à rang réduit, récursif en distance et utilisant un développement de Taylor
Bibliographic record
Abstract
Dans cet article, on teste et on compare, sur des signaux fournis par la Direction Générale de l'Armement Maîtrise de l'Information (DGA/MI), des algorithmes de traitement adaptatif spatio-temporel (STAP) récemment développés par les auteurs pour éliminer l'effet du fouillis lorsque l'on veut détecter une cible lentement mobile au sol par un radar aéroporté. Ces algorithmes sont i) à rang réduit afin de permettre une convergence, en nombre de données secondaires nécessaires à l'estimation de la matrice de covariance, réduite par rapport à la méthode standard du SMI ; ii) fondés sur un développement en séries de Taylor du premier ordre du sous-espace fouillis pour tenir compte d'une éventuelle non stationnarité de ces données secondaires ; iii) récursifs en distance pour limiter la complexité calculatoire. Il apparait que ces algorithmes ont de très bonnes performances et une complexité calculatoire linéaire par rapport au nombre de paramètres. ABSTRACT. In this paper, we test and compare, in the case of the signals given by the Direction Générale de l'Armement Maîtrise de l'Information (DGA/MI), some algorithms recently proposed by the authors to compensate clutter in order to detect, from an airborne radar, targets slowly moving on the ground. The proposed algorithms make use of i) rank reduction in order to reduce the number of secondary snapshots necessary to estimate the data covariance matrix compared to the classic SMI method ; ii) a Taylor series expansion of the clutter subspace in order to compensate for a possible range non stationarity of the data ; iii) a range recursivity in order to reduce the computational complexity. It appears that the proposed algorithms yield a good performance with a computational complexity which is linear with respect to the number of parameters.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".