Parameter Estimation in Radar K-Clutter Plus Noise Based on Otsu’s Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
In a previous work, it has been shown that the application of a modified fractional order moment (MFOM) estimator leads to the same accuracy as the [zlog(z)] method with lower computation complexity. However, undesirable estimation performances have been observed for single look data, low sample sizes and large values of the K-distribution shape parameter. Moreover, the application of positive and negative order moments estimators (PNOME) has a serious impact on the estimation accuracy of the shape parameter. To reduce this sensitivity, it is important to apply thresholding approaches in the case of a single pulse transmission. To this effect, single and double thresholding estimators are proposed in this paper and the Otsu's algorithm is used to compute underlying thresholds. On the basis of Monte-Carlo simulation, the performances of the proposed estimators are assessed against moments and [zlog(z)] methods. Experiment examples indicate that the thresholding approaches based on the Otsu's algorithm is more accurate with computational advantages than existing estimators.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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