Threshold computation for the summation CFAR detector: non-overlapped versus overlapped FFT processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The summation CFAR detector is widely used for the detection of narrowband signals. The normalized detection threshold determines the detection performance and must be appropriately chosen in practical applications. However, numerical problems often occur in the theoretical computation of the normalized detection threshold, particularly when channel power estimates are summed over a large number of overlapped input data blocks. This paper shows that the correlation between power estimates at the output of an FFT filter bank for successive input data blocks can be neglected for most common windows when the over-lap ratio is less than or equal to 1/2. Under this constraint the normalized detection thresholds computed for overlapped and non-overlapped input data blocks are practically identical and the results for the probability of false alarm for a given threshold derived for non-overlapped input data blocks are applicable to overlapped input data blocks. This substantially simplifies the problem of computing the normalized detection threshold for overlapped input data blocks.
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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