Ionospheric and auroral clutter models for HF surface wave and over‐the‐horizon radar systems
Bibliographic record
Abstract
The detection performance of high frequency surface wave radar (HFSWR) and high frequency over‐the‐horizon radar (OTHR) systems is heavily influenced by the presence of radar clutter. In HFSWR systems, the clutter has its origins in vertical‐incidence ionospheric reflections, whereas in OTHR systems, the origin is Bragg backscatter from plasma structures in the auroral zone. This paper models the spreading of the radar clutter signal in the Doppler and angle‐of‐arrival domains that arises from forward‐scattering effects as the radar pulse propagates through regions of ionospheric plasma irregularities. The models use a geometric optics approach to determine the power spectrum of the radar signal phase. This power spectrum is then used to simulate three‐dimensional space‐time‐range radar data cubes. The accuracy of the models is tested by comparing the simulated data to measured data cubes. As an application, the data are then used to evaluate the performance of the newly developed fast fully adaptive (FFA) space‐time adaptive processing (STAP) scheme to improve the extraction of target echoes from a clutter background.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".