A nonlinear approach to ocean wave spectrum extraction from bistatic HF-radar data
Bibliographic record
Abstract
In this thesis, a new approach to the extraction of the directional ocean wave spectrum from bistatic high frequency (HF) radar data is proposed. The proposed method relies on the simplification of the second-order bistatic radar cross-section, analogous to the one presented by Shahidi and Gill [1] for the monostatic case, to facilitate the use of nonlinear optimization methods, such as regularized nonlinear least-squares. Initially, the historic development of the techniques related to the extraction of the ocean wave spectrum from HF radar data is provided in order to contextualize the work of this thesis. Then, an overview of the theory related to ocean waves and the bistatic radar cross-section is shown. Later, the nonlinear optimization method used in this thesis, Tikhonov regularization in Hilbert spaces, is explained, as well as the theoretical background necessary to understand the method. Once the theory is laid out, the simplification of the second-order bistatic HF radar cross section is presented. The simplification consists of a change of variables that allow the use of the “sifting” property of the Dirac delta function. This reduces the dependence of the second-order bistatic cross-section to a single variable. After the simplification process is shown, the methodology for extracting the directional ocean wave spectrum from bistatic HF radar data is discussed. As a proof-of-concept, the method is initially applied to the second-order bistatic cross section, without the presence of noise. The method successfully extracted the directional ocean wave spectrum without assuming any function model for the nondirectional ocean wave spectrum, and assuming a cosine-power model for the directional spreading function. Next, the first-order bistatic HF radar cross section is added to the second-order cross section, and the proposed method is applied, still without noise present. The proposed method was also able to extract the directional ocean wave spectrum and very low error is added by the inclusion of the first-order cross section. Finally, different levels of noise are added to the cross section including the first and second- orders, and the presented method is applied for the extraction. Again, the method yields good results, with acceptable levels of error for the different noise levels. This new approach to the extraction of the directional ocean wave spectrum from bistatic HF radar data presents, to the author’s knowledge, the first nonlinear extraction method for bistatic HF radar data. Further developments of the technique, such as the use of different nonlinear extraction methods, or a general directional spreading function, are suggested.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 0.002 |
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".