Deep Learning for Radar-Based Target Classification
Bibliographic record
Abstract
This study explores the application of deep learning techniques for classifying targets such as people, drones, and cars using micro-Doppler radar signals. Micro-Doppler radar excels in capturing subtle motion features critical for target differentiation. We conduct a comparative analysis of several deep learning architectures, Convolutional Neural Networks (CNN), DenseNets, and Vision Transformers (ViT), together with classical preprocessing (FFT, PCA, SVD). Our focus is on lightweight models optimised for real-time embedded deployment. The SVD-augmented CNN (rank-6 SVD with 3-frame stacking) delivers the best mean accuracy of 98.19 % (5-fold), surpassing our lightweight baseline CNN (98.12 %) and DenseNet (98.05 %), with ViT at 94.33 %. FFT in the preprocessing pipeline further enhances frequency-component extraction for stability. These advances improve the accuracy-efficiency trade-off in radar target classification and support practical deployment in surveillance and autonomous navigation scenarios.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".