Multi-Sensor Adaptive Birth for Labeled RFS Filters using Bistatic Range-Only Measurements
Why this work is in the frame
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Bibliographic record
Abstract
Recently, a Monte Carlo importance sampling-based approach has been established to achieve scalable, multi-sensor, measurement adaptive track initialization for labeled random finite set filters. However, previously suggested proposal distributions require every sensor's measurement function to have a differentiable inverse in the observable dimensions of the target's state space. This assumption is valid for measurement modalities such as position or angle-range sensors, but not for many common non-invertible measurement modalities such as bistatic range-only, angle-only, and range-only sensors. This paper provides an alternative proposal distribution for Monte Carlo importance sampling-based, multi-sensor, measurement adaptive track initiation that is not restricted to invertible measurement functions. The solution for a bistatic range-only measurement function is provided, and simulation results are shown to verify the efficacy of the solution.
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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 | 0.001 |
| 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) | 0.000 | 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