Automatic Radar Modulation Classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automatic modulation classication is concerned with identifying the modulation present on a radio wave. This can be any type of radar or communication signal. It is employed in elds such as cognitive radio for communications, radar analysis for electronic warfare. This thesis is dedicated to classifying a variety of modulations used in modern radar. These include unmodulated, various types of frequency modulation, and phase shift keyed waveforms. This task is accomplished through feature extraction and machine learning techniques. The objective is to determine a suitable method applicable for real-time implementation in a complex electronic warfare environment. Three techniques are proposed: a decision tree combined with Multilayer Perceptron Neural Network, a Multilayer Perceptron Neural Network, and a Convolutional Neural Network. The simulation results show that the decision tree achieves a low classication performance, the Multilayer Perceptron achieves good results in a controlled environment, while the Convolutional Neural Network achieves good generalizable results. The eects of noise, pulse width, and frequency changes are discussed. Each systems latency is also examined. List of Tables 1 Constant Signal Generator Parameters . . . . . .
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it