Novel Hilbert Spectrum-Based Specific Emitter Identification for Single-Hop and Relaying Scenarios
Bibliographic record
Abstract
A novel approach for specific emitter identification using Hilbert spectrum is proposed for both single-hop and relaying scenarios. In particular, two features, i.e., the energy entropy and color moments, are extracted from the Hilbert spectrum of the signal of interest as identification features. The spectrum is obtained through the Hilbert-Huang transform, which is a powerful tool for the analysis of non-linear and nonstationary signals by decomposing them into a set of intrinsic mode functions. The identification task is solved by applying the support vector machine. We further extend the identification problem to a relaying scenario, in which the fingerprint of different emitters may be contaminated by the relay's fingerprints. To the best of our knowledge, this case has not been investigated so far in the literature. At last, simulation results validate that the proposed approach can effectively cope with the specific emitter identification problems in both single-hop and relaying scenarios.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".