Coherent Ofdm Radar Backscatter Modelling for Drones
Bibliographic record
Abstract
The goal of this research is to derive a method for predicting pulsed Orthogonal Frequency Division Multiplexing (OFDM) radar backscatter signal reflected off of dark Uncrewed Aerial Vehicles (UAV)s; “dark” meaning the UAV does not actively emit electromagnetic radiation. Modeling radar backscatter from UAV movement in any orientation and position is challenging as the UAV's local positions and micro-velocities must continually be updated as the simulation progresses in time. The proposed mathematical model simplifies the process of constructing these simulations by introducing reference frames anchored to specific points on the UAV and making logical simplifications. The proposed mathematical model allows a simulation designer to construct the radar target in any arbitrary topology to generate micro-Doppler signatures and range-responses with a closed-form solution. The result of the model is compared to standard simulation techniques which have been shown to match real-world data, showing that the model is accurate.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".