Expanding Window Dynamic-Programming-Based Track-Before-Detect With Order Statistics in Weibull Distributed Clutter
Why this work is in the frame
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Bibliographic record
Abstract
This article considers radar detection and tracking of weak fluctuating targets using dynamic programming (DP)-based track-before-detect (TBD). The clutter is modeled using a Weibull distribution, and the well-known Swerling type 0, 1, and 3 targets are considered. An efficient algorithm is proposed, which employs order statistics in DP-based TBD to detect weak fluctuating targets. In addition, a novel expanding window track-before-detect (EW-TBD) technique for multiframe processing is presented to improve the detection performance with reasonable computational complexity compared to batch processing. It is shown that EW-TBD has lower complexity than existing multiframe processing techniques. Simulation results are presented, which confirm the superiority of the proposed expanding window technique in detecting targets even when they are not present in every scan in the window. In addition, the throughput of the proposed technique is higher than that with batch processing.
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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