Multi-sensor multi-target tracking using out-of-sequence measurements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Out-of-sequence measurements (OOSMS) arise in a multi-sensor central-tracking system due to communication network delays and varying preprocessing times at the sensor platforms. During the last few years a great deal of research has focussed attention on the OOSM filtering problem. However, research in the multi-sensor multi-target OOSM tracking involving data association, filtering, and hypothesis management is still lacking. Some previous efforts have used buffering and measurement reprocessing to handle the OOSMs. In this paper, we present single-model multiple-lag OOSM algorithms for data association, likelihood computation, and hypothesis management for a dwell-based multi-sensor multi-target multi-hypothesis tracking (MHT) system that handles missed detections and clutter. We present numerical results using simulated multi-sensor ground moving target indicator (GMTI) radar measurements.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it