Detecting aircraft in auroral clutter by HF sky wave radar using 2D continuous wavelets
Bibliographic record
Abstract
Operation of sky wave radar in Canada is challenged by the presence of ionospheric clutter from the earth's auroral zone due to severe Doppler spreading, which results in clutter-limited detection of aircraft. Elevation angle control in the transmit and/or receive beams to separate the target can mitigate the auroral clutter to some degree, but may not be always effective especially when operating at high latitudes or near polar regions. This paper reports using the two-dimensional (2D) continuous wavelet transform (CWT) analysis to ‘unearth’ a target buried in the auroral clutter. The approach is based on the ability of the CWT to provide detailed local spectral information about a signal for arbitrary scale, orientation, and physical location. Results obtained using real clutter signals with simulated targets are reported. Very promising initial results, which show an improvement of the signal-to-clutter ratio (SCR) of 45–58 dB, are presented. However, a closer look indicates that these ‘target detections’ are in reality due to wavelets' ability to detect extremely small signal discontinuities introduced during the embedding of synthetic targets in the range compressed data. When inserting a synthetic target directly in the raw data before the range compression, the SCR improvement is shown to be only marginal.
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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 | 0.001 | 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 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".