Time and Accuracy Trade-Off of LPI Radar Classification Technology Based on Time-Frequency Analysis and Deep Learning
Bibliographic record
Abstract
Technology for classifying low probability of intercept (LPI) radar signals with speed and accuracy is critical for cognitive communication research. We used time-frequency analysis (TFA) and deep learning to classify 12 typical LPI radar signals. Traditional methods use the Choi-Williams distribution (CWD), which requires more than 500 times longer TFA generation time than the spectrogram method. In this paper, we show the trade-off relationship between classification accuracy and detection time using a spectrogram, Wigner-Ville distribution (WVD), and CWD as the training datasets. As a result, the CWD model showed higher accuracy than the spectrogram model, but the prediction time was more than 200 times longer. The accuracy difference was only 1 %p for an SNR over −2 dB, but it reached 7.5%p for an SNR of −10 dB. Therefore, a lower SNR shows a distinct trade-off between prediction time and accuracy, depending on the type of TFA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".