Application of fast projection techniques without eigenanalysis to STAP
Why this work is in the frame
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Bibliographic record
Abstract
In ground surveillance from an airborne or space-based radar it is desirable to be able to detect small and slowly moving targets, within severe ground clutter. For operational moving target indication (MTI) systems the clutter filter coefficients have to be updated frequently due to rapidly changing interference environment. This paper examines the small sample size performance of different fast fully adaptive space-time processors (STAP) and compares it to the optimum-detector performance. These previously proposed techniques, named matrix transformation based projection (MTP) and lean matrix inversion (LMI), were originally developed to provide fast jammer suppression in phased array radars with many elements. For this application they have been proven to operate with near-optimum performance, yet with a computational expense extremely reduced from that of the optimum detector in most practical cases. The investigation herein focuses on the performance achieved when only a very few data samples are available to adapt (update) the clutter filter coefficient.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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