Leveraging RF Signal Transformation and Sigmoid Calibration for Optimized Drone Class Prediction
Why this work is in the frame
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Bibliographic record
Abstract
With the increasing use of drones in various sectors, accurately identifying drone types has become crucial for security and surveillance systems. This paper presents a novel RF-based framework for accurately classifying drone types. The proposed approach integrates advanced feature extraction techniques to extract discriminative information from raw RF signals, followed by a sigmoid calibration layer to enhance the confidence and reliability of multiclass predictions. This calibration layer addresses the inherent challenges in multiclass classification by adjusting the predicted probabilities to reflect true likelihoods more accurately. By meticulously analyzing the extracted features and refining the classification models, our goal is to significantly improve the prediction accuracy and robustness of drone-type identification. Experimental results demonstrate the efficacy of our proposed method in effectively distinguishing between various drone classes, thereby contributing to enhanced drone detection and identification systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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