Multi target tracking algorithm based on Lagrangian Relaxation method
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
Multi Target Tracking (MTT) capability of radar increases the extent of control over land and sky. It is a challenging task to provide a coherent air picture to the radar controller in every scan. Multi Sensor Multi Target (MSMT) Data Association (DA) is an important task in an automated Command and Control (C2) system for any Air Defence system. In DA process multiple tracks received for multiple targets from a set of sensors are processed to correlate tracks to targets. The results of DA form a crucial functionality of multi sensor data fusion which is used in target engagement. Multiple Hypothesis Tracking (MHT) methods are very good DA techniques for conflicting scenarios but are complex in design and implementation. A solution methodology is proposed combining Lagrangian Relaxation, dynamic programming and multidimensional assignment approaches.
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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 | 0.000 | 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) | 0.001 | 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 it