Fully digital multi‐frequency compact high‐frequency radar system for sea surface remote sensing
Bibliographic record
Abstract
Compared with single‐frequency high frequency surface wave radar (HFSWR), a multi‐frequency (MF) system provides more feasibility in sea surface dynamic parameters measurement and target detection. In this study, a novel multi‐frequency compact HFSWR based on a fully digital architecture is developed. This system employs a flexible signal processing procedure with low hardware complexity. Without changing the circuit, it can realise two typical multi‐frequency schemes, including time‐division MF (TDMF) and frequency‐division MF (FDMF). Furthermore, a waveform selection criterion is proposed by analysing the difference between the TDMF and FDMF schemes in frequency‐modulated interrupted continuous wave (FMICW). The system performance is preliminarily validated in two frequencies by both close‐loop test and field experiment. It is shown that the range processing of two frequencies are coherent with an amplitude variation <0.005 dB and a phase variation <0.02° over a coherent integration time of ∼8.5 min. Moreover, the sea surface radial current speed measured by two radar frequencies agree (well) with each other with a root‐mean‐square difference of ∼10 cm/s. The accuracy of current speed is verified by buoy data, with an overall correlation coefficient >0.93 and a root‐mean‐square error between 11.0 and 13.0 cm/s.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".