An Lfm Radar Signal Source Identification Method With RFF Drift Robustness
Why this work is in the frame
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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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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