Time-Frequency Analysis and Type Identification of High-Density Communication Countermeasure Electronic Signals
Bibliographic record
Abstract
With the development of communication technology, the electronic signals of communication countermeasures become more and more complex. Facing the increasingly complicated and changeable environment of such signals, the existing cluster analysis algorithms cannot identify the type of communication countermeasure electronic signals ideally. Therefore, this paper carries out the time-frequency analysis and type identification of high-density communication countermeasure electronic signals. The signals were analyzed in both time and frequency domains to extract the time frequency features of the signals more accurately. In this way, the authors captured the variation of non-stationary communication electronic signals in both time and frequency domains. Next, the pulse repetition interval (PRI) transform, a typical pulse repetition frequency (PRF) type identification algorithm, was modified, and applied to the type identification of communication electronic signals, followed by an illustration of the type identification of high-density communication countermeasure electronic signals. After that, several experiments were carried out to compare the absolute errors and relative errors of different time-frequency analysis methods, and contrast the effects of the original and modified PRI transforms, revealing the effectiveness of the proposed approach.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".