Optimizing Multi-Target Tracking Through Airborne Passive Sensor Management
Why this work is in the frame
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Bibliographic record
Abstract
This article presents a novel approach to multi-target tracking utilizing airborne passive sensors by leveraging BR-AOA data. Initially, a three-dimensional target tracking model based on BR-AOA information is developed, with the application of a GM-PHD filter facilitating passive tracking for multiple targets. Subsequently, sensor management techniques are employed to enhance the accuracy of three-dimensional motion-state estimation for multi-target scenarios. The Cauchy-Schwarz divergence is adopted as the objective function, guiding the selection of control commands for the sensor in real-time during the tracking process, aiming to maximize CS divergence and optimize sensor positioning. Numerical simulations demonstrate the efficacy of the BR-AOA multi-target tracking method in accomplishing tracking tasks. Furthermore, compared to a fixed sensor configuration, the proposed sensor management scheme significantly reduces the OSPA distance and enhances positioning accuracy.
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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 | 0.000 |
| Open science | 0.000 | 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