Ship Motion and Wave Radar Data Fusion for Shipboard Wave Measurement
Bibliographic record
Abstract
Defence Research and Development Canada (DRDC) Atlantic has conducted many dedicated seakeeping and structural load trials on the Canadian Navy research ship CFAV Quest and on several Canadian Navy warships. Typically, wave buoys have been deployed to measure seaway wave characteristics; however, there has been an ongoing interest in evaluating shipboard wave measurement systems. These systems have some advantages over wave buoys for short-term trials and are needed for longer-term sea trials and to provide wave input data for tactical and real-time ship operator guidance systems. This paper presents some of our experiences with wave radar. In the last few years there have been significant advances in wave radar technology (systems that extract wave data from backscatter information contained in the video output of X-band navigational radar displays). Commercial "off-the-shelf" systems are now available. While there is evidence that these systems can provide reliable wave data from shore-based or stationary platform installations, it is DRDC's experience on a ship moving in waves, that wave radars can give good direction and frequency measurements but less reliable wave heights. DRDC has developed a method to improve shipboard wave height measurement through fusion of wave radar data with measured ship motion response data. This paper discusses the development of the wave data fusion process, validated through previous sea trial data, and presents the results of a recent demonstration of the approach during a sea trial conducted on CFAV Quest in November/December 2008.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".