Specific Emitter Identification Based on Nonlinear Dynamical Characteristics
Bibliographic record
Abstract
Specific emitter identification (SEI) designates the unique transmitter of a given signal, using only external feature measurements called the RF fingerprints of the signal. SEI is often used in military and civilian spectrum-management operations. The SEI technique has also been applied to enhance the security of wireless network, such as VHF radio networks, Wi-Fi networks, cognitive radios, and cellular networks. A novel SEI method based on nonlinear dynamical characteristics is proposed in this paper. The method works based on the actual signal's inherent nonlinear dynamical characteristics. The permutation entropy is extracted as the signal's RF fingerprint to identify the unique transmitter. The quadrature phase-shift keying (QPSK) signals from four wireless network cards and differential quadrature phase-shift keying (DQPSK) signals from three digital radios are utilized to evaluate the performance of the method. Experimental results demonstrate that the proposed method is effective. On the other hand, the proposed method is convenient to implement in a PC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".