Track-Before-Detect Strategies for Radar Detection in G0-Distributed Clutter
Why this work is in the frame
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Bibliographic record
Abstract
This paper considers target detection via dynamic-programming based track-before-detect (DP-TBD) for radar systems. The clutter is modeled usingenlr G0 distribution, which is usually used to model clutter received from high-resolution radars and radars working at small grazing angles. Two target models, namely, Swerling 0 and 1 models, are considered to capture the radar cross section changes over time. DP-TBD techniques that integrate amplitude suffer from significant performance loss in this case due to the high likelihood of target-like outliers. In this paper, the log-likelihood ratio (LLR) is used in the integration process of DP-TBD, taking the place of amplitude, to enhance radar detection performance. The expressions for the LLR for the above target models are derived first. However, neither of them has a closed-form solution. In order to reduce the complexity of evaluating the LLR, efficient but accurate approximation methods are proposed. Then the approximated LLR is used in the integration process of DP-TBD. Simulations are used to examine the efficiency of the approximation methods as well as the performances of different DP-TBD strategies.
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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 | 0.001 | 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