A Low-Complexity Nonparametric STAP Detector
Bibliographic record
Abstract
Phased array radars use space time adaptive processing (STAP) to detect targets in angle, range, and speed using an adaptive weight vector that depends mainly on the covariance matrix of the cell under test (CUT). This covariance matrix is estimated from the secondary cells surrounding the CUT under the assumption of homogeneous clutter and noise background. However, these secondary cells are often contaminated by multiple discrete interferers, targets or combination thereof, which degrade the estimation of the CUT's covariance matrix and, in turn, the detection performance. In this paper, we address the problem of detecting the nonhomogeneous secondary cells that need to be excluded from the adaptive weight calculation. We introduce a nonparametric and covariance-free alternative to the normalized adaptive matched filter (NAMF) test that does not need the tedious estimation process of the covariance matrix matrix of secondary cells nor prior knowledge about the interference distribution. Consequently, the computational complexity of the weight vector is reduced, which is of a great importance for real-time operation of radar systems. The equivalent robust performance of the proposed test compared to the NAMF test is demonstrated through simulations under different clutter scenarios and operation conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".