FFT filter bank based majority and summation CFAR detectors: a comparative study
Bibliographic record
Abstract
The FFT filter bank with CFAR (constant false alarm rate) signal detection is an efficient method for detecting narrowband signals in noise. A common technique for improving detection performance involves the summation of the power spectral information over L successive signal data blocks. This L-block summation detector basically amounts to a form of noncoherent time integration. An alternative approach for processing multiple data blocks is the J-out-of-L detector. While the J-out-of-L detector is known to be sub-optimal for an additive white Gaussian noise channel, it has a more robust false alarm rate performance in the presence of impulsive noise. Consequently, a thorough understanding of the relative performance of the L-block summation and J-out-of-L detectors is useful for selecting the best detector for a given application. The paper presents a comparative performance analysis for Gaussian noise. It shows that: (1) the best performing of the L J-out-of-L detectors is the ([L/2]+1)-out-of-L detector called the L-block majority detector ([x] = integer part of x); (2) the L-block majority detector can approach within 1 dB of the performance of the L-block summation detector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".