An Lfm Radar Signal Source Identification Method With RFF Drift Robustness
Bibliographic record
Abstract
Linear frequency modulation(LFM) signals are widely used in radar technology. Accurate identification of LFM signal sources is of great significance. Radio frequency fingerprinting(RFF) is a non-cryptographic authentication method based on hardware physical differences, which is unique and therefore widely used for signal source identification. However, in practical applications, the RFF of radar signal sources generates heat after prolonged operation. This causes a certain degree of drift in the RFF within and between pulses, thereby affecting recognition accuracy. To address this issue, this paper proposes an LFM radar signal source identification method with RFF Drift Robustness(RDR). RDR first uses a frame-based coherent accumulation method to separately calculate the RFF of the front and rear halves of the pulse, then introduces a Class Principal Component Analysis layer(PCALayer) to mitigate the impact of RFF drift within the pulse. Subsequently, an Long Short-Term Memory(LSTM) Network is used to capture the temporal dependency of RFF between pulses, thereby enhancing the model's robustness to RFF drift between pulses. Experimental results demonstrate that the proposed method exhibits excellent robustness to RFF drift in real-world 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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".