Detection of linear chirp and non-linear chirp interferences in a spread spectrum signal by using Hough-Radon transform
Bibliographic record
Abstract
The time-frequency distribution (TFD) of a spread spectrum signal looks more like a noise, and the energy distribution occupies the full two-dimensional time-frequency (TF) plane. Any jammer or interference will be well localized in the TF plane. By treating the TF plane as an image, the interference patterns can be detected by using the image analysis technique of Hough-Radon transform (HRT). Curves with mathematical equations can be easily detected by transforming the shapes into Hough domain, and searching for dominant peaks (maximum values). The co-ordinates of the dominant peaks provide the parameters of the shape. For example, in case of a straight line, the Hough domain would be the “rho, theta” space, where “rho and theta” are the parameters of a straight line. The maximum value in the rho, theta plane would correspond to the exact parmeters of the straight line. If a high resolution TFD for a spread spectrum signal is achieved, then any linear chirp or non-linear chirp interference will show up as straight lines and curves in the TF plane. By applying the HRT on the TF plane, chirp interferences can be identified. Evaluation of the proposed techniques show successful detection of both linear and hyperbolic (nonlinear) chirp interferences in spread spectrum signals even under very low SNR conditions of 0 dB. The method detects any localized interference as along as the interference pattern in the TF plane can be represented by a
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".