Recognition of LPI Radar Signal Intrapulse Modulation Based on CNN and Time-Frequency Denoising
Why this work is in the frame
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Bibliographic record
Abstract
Aiming at the problem of low probability of intercept (LPI) radar signal recognition accuracy under low signal-to-noise ratio (SNR), a method for LPI radar signal recognition based on convolutional neural network (CNN) and time-frequency denoising is proposed. Firstly, the Smoothed Pseudo Wigner-Ville Distribution (SPWVD), which performs well under low SNR, is applied for time-frequency analysis of radar signals. Then, a frequency domain filter is designed using the K-means clustering method to reduce noise in the signal. Finally, the basic structure of the CNN network is studied, and a CNN network structure is designed and developed for the proposed LPI radar signal recognition system. Suitable hyperparameters are determined for it through parameter tuning. Time-frequency images are input into the CNN network to extract and learn deep features for radar signal recognition. Experimental results show that when the SNR is -8 dB, the overall recognition accuracy of 12 kinds of LPI radar signals reaches 91.67% using this method.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.000 | 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