Derivation of a formula for the probability of false alarm for the FFT filter bank-based J-out-of-L CFAR detector
Bibliographic record
Abstract
A formula is derived for the probability of false alarm (P/sub fa/) for the FFT filter bank-based J-out-of-L CFAR detector for AWGN channels when the input data is overlapped. Practical approximations for P/sub fa/ are then deduced. These approximations are valid when the overlap ratio, /spl gamma/ > 0, does not exceed 1/4 . For the Blackmail and Blackman-Harris windows, when 1/4 /spl les/ /spl gamma/ /spl les/ 1/2 , these approximations still hold. The validity of these approximations has been confirmed by simulation experiments. In addition, results showing the performance improvement that can be obtained from data overlapping for a given observation time are also given.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".