Improving Precision in the Reference Velocity of ADCP Measurements Using a Kalman Filter with GPS and Bottom Track
Bibliographic record
Abstract
Global positioning system (GPS) data are used to measure boat velocity during acoustic Doppler current profiler (ADCP) discharge measurements, particularly when bottom tracking (BT) is biased by moving bed. A Kalman filter is developed to improve the velocity reference used by the ADCP under such conditions. Kalman filtering is a recursive statistical technique that estimates the current state of a process, given various inputs and their variance. In the case of data obtained by ADCP, the availability of two independent velocity measurements and a position measurement makes this method particularly attractive. The new Kalman filter combines raw inputs for GPS position (GGA) and Doppler velocity (VTG) with BT data in real time to produce best estimates of velocity. The technique is evaluated and calibrated using various accuracies of GPS data collected simultaneously along with unbiased BT data at two different sites. On the Gatineau River, real-time kinematic and wide area augmentation system corrections were used for this study. On the Saint Mary’s River, nondifferential GPS was collected. To examine the conditions under which such a system would be required, synthetic data for a moving bed contamination of BT were created. In all moving bed conditions evaluated, the Kalman filter estimates of reference velocity were superior to raw inputs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".