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.This dissertation utilizes uniform linear array (ULA) MIMO radar systems and proposes two Riemannian geometry-based constant false alarm rate (CFAR) detectors, a direction of arrival estimation technique based on Riemannian mean and distance, and interference-plus-noise covariance matrix estimation for beamforming in a Riemannian space.All proposed techniques exploit the regularized Burg algorithm (RBA) to convert each range bin into a Toeplitz Hermitian positive definite (THPD) matrix, which represents a point on the Riemannian manifold.Although Toeplitz structure is generated from ULA configurations, non linear array configurations would produce non-Toeplitz covariance matrices even if RBA guarantee Toeplitz structure.The proposed Riemannian-Brauer matrix (RBM) CFAR detector is based on the Riemannian distance between the Riemannian mean of the clutter-plus-noise Brauer bound and the THPD covariance matrices of the outliers.Also, the proposed ULA uniform linear antenna WSF weighted spatial filter xvi
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".