Enhancing Space-Time Adaptive Processing Through the Slepian Transform<sup>1</sup>
Bibliographic record
Abstract
A High Dynamic Range Space-Time Adaptive Processing (HDR-STAP) improves cancellation of large interference sources by using a Fast Slepian Transform to capture and cancel more clutter energy than other STAP approaches. STAP's efficacy relies on its ability to estimate clutter interference across space (antenna elements) and time (pulses) but hits limitations due to interference strength and spectrum spread. In addition to clutter cancellation limits, the target signal can suffer a STAP processing loss. This paper focuses on the signal to clutter enhancement metric to compare performance of different STAP techniques. HDR-STAP projects the estimated clutter covariance matrix onto the Slepian basis functions. A limited number of orthogonal Slepian basis functions are defined using the signals' sample rate and a selected finite bandwidth. HDR-STAP takes advantage of a Fast Slepian transform (FST) from Karnik, et. all [1], producing a practical implementation. This article demonstrates that HDR-STAP removes more clutter than sample matrix inversion (SMI) and Eigendecomposition STAP while achieving signal losses below those predicted by the Reed, Mallet, and Brennon (RMB) rule.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".