FFT filter bank based majority and summation CFAR detectors: a comparative study
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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