Localization of a point target from an optical sensor's focal plane array
Why this work is in the frame
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Bibliographic record
Abstract
This paper considers the localization of a point target from an optical sensor's focal plane array (FPA) with a dead zone separating neighboring pixels. The Cramer Rao lower bound (CRLB) for the covariance of the maximum likelihood estimate (MLE) of target location is derived based on the assumptions that the energy density of the target deposited in the FPA conforms to a Gaussian point spread function (PSF) and that the pixel noise is based on a Poisson model (i.e., the mean and variance in each pixel are proportional to the pixel area), . Extensive simulation results are provided to demonstrate the efficiency of the MLE of the target location in the FPA. Furthermore, we investigate how the estimation performance changes with the pixel size for a given dead zone width. It is shown that that there is an optimal pixel size which minimizes the CRLB for a given dead zone width.
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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) | 0.001 | 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