Signal Processing Methods in Riemannian Geometry with Application to Drone Detection
Bibliographic record
Abstract
The number of drones manufactured by many companies, such as DJI, Parrot, and 3D-Robotics, is always on the rise. Drones are widely used for commercial purposes, such as the delivery of goods, surveying and monitoring public places. On the other hand, drones can also be used to perform terrorist attacks or can be used to transport illegal drugs. Thus, a fast and reliable drone detection technique is very much needed to allow enough time for countermeasures in critical situations. Drones are considered complex targets which can range in size from 10 m 2 to 0.01 m 2 with symmetrical shape and fluctuating radar cross section (RCS), hence low signal-to-interference-plusnoise ratio (SINR). Current radar systems with classical signal processing techniques might fail to detect drones in low SINR environments with limited number of received snapshots. Multiple-input multiple-output (MIMO) radar systems with signal processing methods in Riemannian space can be exploited to improve the probability of drone detection, enhance the robustness of the direction of arrival estimation and improve the minimum variance distortionless response beamforming by estimating the interference-plus-noise covariance matrix in Riemannian space.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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 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".