Enhanced UAV Detection and Classification Using Machine Learning and MIMO Radars
Bibliographic record
Abstract
In the present investigation, the impacts of antenna field of view (FOV) on the accuracy of machine learning (ML) models utilized for the classification of various unmanned air vehicle (UAV) types were systematically explored using full-wave electromagnetic simulation software. Initially, similar to many state-of-the-art works, an ML algorithm was meticulously trained under a particular condition where the relative angle between the UAVs and the antenna was kept at 0$^{\circ}$, yielding an accuracy of 96.5%. In contrast to the common practice, the trained ML model was subsequently subjected to testing at varied relative angles, spanning from 20$^{\circ}$to 90$^{\circ}$. Observational outcomes delineate a decrease in ML classification accuracy with an increase in the relative angle between UAVs and the antenna. Next, this study investigates the impact of utilizing multiple-input-multiple-output (MIMO) radar system for classification. The results indicate enhancement of UAV detection and classification efficacy when contrasted with a single-input-single-output (SISO) radar system, at 80$^{\circ}$, an accuracy of 60% for the MIMO antenna compared to 13% for the SISO antenna. To corroborate the results obtained by utilizing full-wave electromagnetic simulation software, a series of experimental laboratory measurements were undertaken. The empirical measurements were found to yield comparable ML accuracy, at 80$^{\circ}$, an accuracy of 38% for the MIMO antenna compared to 1% for the SISO antenna. This work underscores the paramount versatility achieved through invoking full-wave electromagnetic simulators alongside ML algorithms and the respective possible impact on standard practices when it comes to developing next-generation MIMO radar systems for UAV classification.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".