Machine Learning Algorithms for Identification of Low Signal-to-Noise Ratio Radar Signals
Bibliographic record
Abstract
Areas such as spectrum monitoring require identification of known and unknown radar transmitters to identify known and rogue users.Often such identification needs to done under conditions where the signal-to-noise ratio is low.This thesis proposes an approach to determine the unknown radar chirp parameters of a linear frequency modulated (LFM) radar waveform, assuming that the unknown parameters come from a given set of known chirp parameters.A concatenated output of matched filters corresponding to the known set of chirp parameters is presented to four well-known machine learning architectures, namely decision tree (DT), random forest (RnF), naïve Bayes (NB) and support vector machine (SVM).Realistic radar parameters for airborne, marine and weather radars were used in the simulations.The robustness of the classifiers to parameter mismatch and truncation of the radar pulse were also studied.DT outperformed the other classifiers except for the truncated pulse case (where NB and SVM performed better).RnF did not perform acceptably.6 Conclusions and Way Forward 6.1 Limitations . . . . . . . . . . . . . . . . . .
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".