Gaussian Process Regression for Empirical Radar Cross Section Modeling Based on the SCATR ISAR Dataset
Bibliographic record
Abstract
Empirical modeling of Radar Cross Section (RCS) is crucial in defence applications, particularly for guiding the design of specialized Ship Detection Modes (SDMs) tailored for small vessels. This paper presents a novel Gaussian Process Regression (GPR) model for RCS prediction of ships in Inverse Synthetic Aperture Radar (ISAR) imagery. Leveraging 586 Motion Compensated (MO-COMP) ISAR frames representing five ship classes from the real-world Small Craft Automatic Target Recognition (SCATR) dataset, three variants of (i) baseline Multiple Linear Regression (MLR) model, and (ii) GPR model, are constructed. The demonstrated efficacy of the GPR-based approach surpasses that of the baseline MLR model, emphasizing its significant improvement in RCS modeling.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".