Feature-aided tracking in dense clutter using the Clutter09 data set
Bibliographic record
Abstract
A feature-aided multihypothesis tracker (MHT) was used to process bistatic sonar data from the Clutter09 sea trial. The trial used a towed echo repeater that modified and retransmitted several variations of an incident signal including one that was intended to mimic interaction with a hollow steel cylinder. An automated aural classifier evaluated each of the contact signals in terms of its time-frequency and spectral characteristics, producing a set of feature values for each contact. The tracker used a subset of the feature information in its track score calculation to encourage the association of contacts that were similar in nature to the specified target. Comparisons of the baseline and feature-aided cases in three scenarios showed that the use of feature information in the tracker resulted in a factor of six reduction in the number of false tracks and a one-third reduction in the duration of the false tracks. There was limited impact on the quality of the specified target track. Tracks on other known objects, including the Campo Vega oil rig, were eliminated or degraded to the degree that they were dissimilar to the specific target.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".